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Mapping Vulnerabilities in the Industrial Internet of Things Landscape
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29th CIRP Design 2019 (CIRP Design 2019) 29th CIRP Design 2019 (CIRP Design 2019)
Mapping in Industrial Internet of Things Mapping Vulnerabilities Vulnerabilities in the the Industrial Internet ofFrance Things Landscape Landscape 28th CIRP Design Conference, May 2018, Nantes, Dimitris Mourtzisa*, Konstantinos Angelopoulosa, Vasilios Zogopoulosa
a a a Dimitris Mourtzis *, Konstantinos Angelopoulosand , Vasilios Zogopoulos A new methodology to analyze the functional physical architecture of Laboratory for Manufacturing Systems and Automation (LMS), Department of Mechanical Engineering and Aeronautics, University o f Patras, Laboratory for Manufacturing Systems and Automation (LMS), of Mechanical Engineering and Aeronautics, University o f Patras, RioDepartment Patras, Greece existing products for an assembly oriented product family identification Rio Patras, Greece a a
Paul Stief *, Jean-Yves Dantan, Alain Etienne, Ali Siadat
* Corresponding author. Tel.: +30-2610-910160; fax: +30-2610-997314. E-mail address: [email protected] * Corresponding author. Tel.: +30-2610-910160; fax: +30-2610-997314. E-mail address: [email protected]
École Nationale Supérieure d’Arts et Métiers, Arts et Métiers ParisTech, LCFC EA 4495, 4 Rue Augustin Fresnel, Metz 57078, France
Abstract
*Abstract Corresponding author. Tel.: +33 3 87 37 54 30; E-mail address: [email protected]
With the advent of the fourth industrial revolution manufacturing systems are transformed into digital ecosystems via internet communication With the advent of the revolution are transformed intoamount, digital variety ecosystems via internetofcommunication technologies to form thefourth smart industrial factories of the future.manufacturing Industry 4.0 issystems characterized by increased and complexity the exchanged technologies to form the smart factories of the future. Industry 4.0 is characterized by increased amount, variety and complexity of the exchanged data. Industries utilize a plethora of machines, robots, computers and servers, all connected to each other through different networks. Between Abstract data. Industries a plethora of machines, computers and servers, connected to each other different networks. Between those machines, utilize a massive exchange of data androbots, sensitive information is takingall place. Whenever there is a through new interface between networks and those machines, exchange of dataasand sensitive information takingdevices place. enter Whenever thereofisInternet a new interface between devices, it poses aa massive weak link and is marked potential attack point. Asismore the realm of Things, attacksnetworks aimed atand the Indevices, today’s business environment, themarked trend towards moreattack product variety and customization is unbroken. Due to of this development, the need of poses a weak link and as potential point. As more devices enter the of Internet attacksbreaches aimed atthat the variety ofit Internet-endpoints will isgrow. However, existing internet technologies are plagued byrealm data privacy issues Things, and malicious agile andofreconfigurable production systems emerged to cope with technologies various products and product families. To design and optimizebreaches production variety Internet-endpoints will grow. However, existing internet are plagued by data privacy issues and malicious that will act as a setback for adopters of Industry 4.0 technologies. Thus, the need for identifying the required cybersecurity protocols, at specific systems well as to for choose the optimal product matches, product analysis methods are needed. Indeed, most of the known methods aim to will act as as where a setback adopters of Industry 4.0 technologies. Thus, the need for identifying the The required cybersecurity protocols, specific endpoints data leaks may occur, has been vastly increased to tackle malicious behaviors. true potential of Industry 4.0 at cannot be analyze a product or oneleaks product family on thebeen physical level. Different product families,behaviors. however, may differ largely inofterms of the4.0 number and endpoints where data may occur, has vastly increased to tackle malicious The true potential Industry cannot be achieved, if those challenges are not addressed properly. This work aims to identify and map the potential vulnerable endpoints in a common nature of components. This fact impedes an efficient comparison and choice of appropriate product familyvulnerable combinations for thein production achieved, if those challenges are not addressed properly. This work aims to identify and map the potential endpoints a common industrial paradigm, where data will cross during aggregation and propose a robust way of securing a wireless sensor network (WSN) by ensuring system. A new methodology is proposed toduring analyze existing products in view of their functional and physicalsensor architecture. The aim is cluster industrial paradigm, where data aggregation and a robust waythe of WSN. securing a wireless network bytoensuring the integrity and authenticity ofwill the cross parcels and the identity of thepropose users comprising The vulnerabilities of a data(WSN) acquisition system these productsand in new assemblyoforiented product for the optimization of existing lines and the creation future reconfigurable the integrity authenticity the parcels and families the of the users comprising theassembly WSN. The vulnerabilities of a of data acquisition system applied in the laser machine industry are mapped andidentity presented. assembly on Datum Chain, the structure of the products is analyzed. Functional subassemblies are identified, and applied insystems. the laserBased machine industryFlow are mapped andphysical presented. a functional analysis is performed. Moreover, a hybrid functional and physical architecture graph (HyFPAG) is the output which depicts the © Authors. Published by B.V. © 2019 2019 The The Authors. Published by Elsevier Elsevier B.V. similarity between product families by providing design support to both, production system planners and product designers. An illustrative © 2019 The Authors. Published by Elsevier B.V. committee Peer-review under responsibility of the scientific of Design Conference Conference 2019. 2019 Peer-review under responsibility of the scientific committee of the the CIRP CIRP Design example of a under nail-clipper is used to the proposed methodology. industrial case study on two product families of steering columns of Peer-review responsibility ofexplain the scientific committee of the CIRPAn Design Conference 2019 thyssenkrupp Presta France is then carried out to give a first industrial evaluation of the proposed approach. Keywords: Cybersecurity; IoT; Privacy; Wireless Sensor Networks ©Keywords: 2017 TheCybersecurity; Authors. Published by Elsevier IoT; Privacy; WirelessB.V. Sensor Networks Peer-review under responsibility of the scientific committee of the 28th CIRP Design Conference 2018.
devices to collaborate and exchange data. devices to collaborate and landscape exchange data. However, the diverse of Industry 4.0 leads to However, the diverse landscape Industry to multiple interfaces between networksofand devices4.0to leads support The fourth Industrial revolution has a vision of intelligent multiple interfaces between networks and devices to support The fourth Industrial revolution has a vision of intelligent the different sensors, devices and machines. Each interface and and smart factories, allowing smart systems with autonomous the different deviceshas anddifferent machines. Each interface and smart factories, allowing smart systems with autonomous link betweensensors, the networks vulnerabilities andand is properties to guide manufacturing ecosystems as data 1.properties Introduction of thebetween productthe range and characteristics manufactured and/or link networks has different vulnerabilities and is to guide manufacturing ecosystems as data susceptible to different attacks. With the adoption of Industry monitoring and saving, self-healing and self-configuring. Its assembled intothis system. In this With context, main challenge in susceptible different thethe adoption of Industry monitoring saving,from self-healing andofself-configuring. Its 4.0 the number of theseattacks. links will increase, providing more true potentialand emanates the plethora devices, or Things, Due to the fast development in the domain of modelling and analysis is now not only to cope with single 4.0 the number of these links will increase, providing more true potential emanates from the plethora of devices,oforeveryday Things, attack points to adversaries [2,3] and since existing internet communicating with each other. The introduction communication and each an ongoing trend of digitization and products, a limited product range existing product families, attack points to plagued adversaries [2,3] and since existing internet communicating with other. The introduction of everyday technologies are with dataor privacy issues and breaches, devices to the variety of communication technologies created digitalization, manufacturing enterprises are facing important but also to be able to analyze and to compare products to technologies are plagued with data privacy issues and breaches, devices to the variety of communication technologies created the true potential of Industry 4.0 will not be achieved ifdefine these the realm of Internet of Things (IoT), a core segment of the new the true potential Industry 4.0 will notthat be classical achieved existing if these challenges in today’s market environments: a continuing new product families. It can be observed the realm of Internet of Things (IoT), a core segment of the new challenges are notof addressed properly. Industry 4.0 era. This resulted in the generation of massive challenges areaims not tendency reduction oftoproduct times and product areaddressed regrouped in function of clients or features. Industry 4.0data era.that Thisneeded resulted in propagated the development generation massive This families paper to map theproperly. vulnerabilities in an Industry 4.0 amounts towards of be and of stored, for This paper aims to map the vulnerabilities in an Industry 4.0 amounts of data that needed to be propagated and stored, for shortened product lifecycles. In addition, there is an increasing However, assembly oriented product families are hardly to find. network paradigm and list potential attacks that can be utilized the smart systems and services to harness them [1]. network paradigm and list potential attacks that can be utilized theAnother smart and services to harness them [1]. demand ofsystems customization, being at the same time in a global On the product family level, products differ mainly in two core feature of Industry 4.0 is the communication to disrupt the networks integrity. Moreover, several standards Anotherdifferent core of Industry 4.0the isthe the communication to disrupt the networks integrity. Moreover, several standards competition with feature competitors all over world. This trend, main characteristics: (i) the number ofintegrations components and (ii) the between companies and promotion of and protocols that may support the are listed. A between different the macro promotion of and protocols that may support the integrations are listed. A which is inducing thecompanies development to micro type of components (e.g. mechanical, electrical, electronical). collaborative scenarios. This featureand isfrom enabled by integrating paradigm of an IoT system in production is used, to depict how collaborative scenarios. feature is enabled byaugmenting integrating paradigm of an IoT system in production is used, to depict how markets, results inin diminished lot networks, sizes due to Classical methodologies considering mainly single products cloud platforms the This Industrial which provide the obstacles have been overcome. cloud platforms inand thea Industrial networks, whichand provide thesolitary, obstaclesalready have been overcome. product varieties (high-volume low-volume production) [1]. or existing product families analyze the databases, services securetospace for employees databases, services and a securevariety space as forwell employees To cope with this augmenting as to beand able to product structure on a physical level (components level) which identify in the existing causes difficulties regarding an efficient definition and 2212-8271 possible © 2019 The optimization Authors. Publishedpotentials by Elsevier B.V. 2212-8271 2019responsibility The Authors. of Published Elsevier B.V.of the CIRP Design Conference 2019 Peer-review©under the scientific committee production system, it is important tobyhave a precise knowledge comparison of different product families. Addressing this 1. Introduction
Keywords: Assembly; Design method; Family identification 1. Introduction
Peer-review under responsibility of the scientific committee of the CIRP Design Conference 2019
2212-8271©©2017 2019The The Authors. Published by Elsevier 2212-8271 Authors. Published by Elsevier B.V. B.V. Peer-review under responsibility of scientific the scientific committee theCIRP CIRP Design Conference 2019. Peer-review under responsibility of the committee of the of 28th Design Conference 2018. 10.1016/j.procir.2019.04.201
266 2
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2. State of the Art Advances in manufacturing and communication technologies set the main foundations for transforming current industries into Cyber-Physical Systems (CPS) via the integration of IoT. However, the adoption of IoT in manufacturing results in the generation of massive streams of data that must be propagated in order to be stored and processed [1]. For these data to be used they must be propagated through an API to a cloud platform as plain text, so users can interpret them. The variety of data requires many APIs located in a cloud platform or at the local network. However, these interfaces pose weak links that adversaries could potentially exploit [2]. The foundation of Industry 4.0 is based on the Internet of Things, a network of interconnected devices that create a smart grid. The IoT concept started to affect manufacturing when the cost for its implementation reduced and the various communication technologies evolved rapidly. This combination enabled all sort of devices to be connected and exchange various information. The appliance of IoT in industry includes sensors, actuators, robots, machine-to-machine communications (M2M), Cloud platforms as well as security mechanisms to ensure a secure and reliable exchange of data. In previous years the shop-floor was comprised only by machines that communicated with a single computer via physical connection and exchange data without the risk of adversaries being able to intercept the connection. However, with the introduction of IoT, modern industries are transformed into Cyber Physical Systems (CPS) comprised of numerous interconnected devices and sensors. In the CPS model the main source of generated data is the shop-floor. Nowadays shop-floors are filled with devices embedded with communication technologies that transmit data using various protocols and form a wireless sensor network (WSN). The data from these devices are very valuable and can be used with various services to provide insights for the future of the company and for upgrading the overall shop-floor. This kind of data is distributed both locally and remotely, potentially to users outside the company. Due to the bulk of those data, a propagation must occur first to assemble all data in a storage, in order to be distinguished in two categories: structured and unstructured data. Structured data have distinct length and can be used directly for analysis and prediction models, unlike unstructured data that needs to be processed first to obtain any helpful information. The solution for this pool of data lies on cloud technology. The Cloud layer is one of the pillars of IoT which can provide a storage platform that can gather data from all kind of sources and simultaneously be accessed by any device via internet. The advantage a cloud platform gives to an Industry is the construction of a digital identical industry that will be fed with real time data and run simulations on future predictions, to prevent any unwanted scenarios and suggest modifications and optimization techniques to upgrade the overall performance [4]. These helpful insights are used by employees with access to the cloud layer. The complex character of IoT revolutionized the modern Industry by transforming it to a complex intercommunicating system. For Industries to adopt the IoT, new protocols and architectures must be implemented to monitor, collect and regulate this pool of data. In manufacturing the most reliable method for communicating is setting up a Wireless Sensor
Network (WSN), connecting many sensor devices collaborating to achieve the same goal. The complexity of IoT requires an architecture that can be flexible and adapt to the variety of devices and communication protocols. Numerous protocols have been developed, with the most complete and adopted being the OPC – Unified Architecture (OPC – UA) [5]. The OPC – UA allows servers to provide real time processed and non-processed data to all clients. OPC – UA interoperability enables other systems not familiar with the data model to browse through the server and gather information. The OPC - UA aims to provide interoperability among the diversity of devices and ensure the authority of the users and the authenticity of the exchanged data. In addition, many security mechanisms have been constructed to prevent adversaries from accessing stolen data by encrypting the messages [5]. OPC – UA mainly uses the Advanced Encryption Standard (AES) [25] and the X.509 standard [28] that have proven to be robust security mechanisms for message encryption and integrity. However, for employees to use and understand the data they must be propagated through an application programming interface (API) to a database to be processed, categorized and displayed as plain text. APIs are the endpoint of communication between the various connected devices that comprise the WSN. An API has multiple connections with various devices simultaneously and can receive and sent data from the shopfloor to the factory level and vice versa. It monitors the devices comprising the shop-floor and regulates the flow of data. This aggregation of data at single endpoints poses a lucrative link for adversaries. According to a recent survey by Deloitte 70% of manufacturers transmit personal information through connected devices while only 55% of that information is encrypted [32]. Thus, the misusage of API can be catastrophically for industries leading from stealing/altering data to the death of human resources [6]. Although, the diversity of connected devices and communication protocols is beneficial for industries, it provides an expanded surface of attack points for adversaries [3]. According to the National Institute of Standards and Technology (NIST) guide for conducting Risk Assessments in IT systems, a vulnerability is defined as a ‘flaw or weakness in system security procedure, design, implementation, or internal controls that could result in a security breach or a violation of systems security policy’ [10]. Each protocol has its own different vulnerabilities thus, giving attackers more than own way of intercepting sessions and tampering with data. The introduction of IoT has given the ability to third parties to switch from manipulating data to controlling actuators and machines. The vulnerabilities of IoT networks are the key points of securing IoT in its entity and subsequently transform Industry 4.0 into a more approachable scenario. An industry must be resilient when attacked, minimizing the effects of the attack and restoring security as quickly as possible [32]. Evidence for the necessity of cybersecurity at all layers are in the numbers. According to a recent report by Markets and Markets the Global Industrial cybersecurity market will reach $22.79 billion by 2023 [29], while the average annual loss from cyberattacks for industries ranges from $347,000 to $497,000
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Fig. 1: A typical IoT network
depending on the industry’s size [30]. An Accenture study has shown that the largest cost component from a cyberattack is information loss, accounting for up to 43% of the cost [33]. In addition, the 13th annual report by IBM refers to the influence of IoT devices and AI to data-breach costs [31]. Thus, the need for cybersecurity is of most importance for a healthy transition to the Industry 4.0 realm. In order to secure a network, one needs to map and identify the various vulnerabilities each different link has and apply the most prominent technologies to prevent any unwanted scenario. 3. System Mapping This paper presents an approach for mapping possible weak links in an IoT network based on the vulnerabilities and gaps each industrial communication technology and protocol possess. A typical scenario of interconnected devices in an IoT network can be seen in Fig. 1. The network consists of three main layers: The Shop-floor, the Cloud layer and the Factory layer. The shop floor is the main source of data and consists mainly of sensors, actuators, robots, machines and IoT nodes that generate streams of data. These streams of data must be propagated to the cloud layer or to a DAQ service. The variety of devices on the shop floor implies the integration of a variety of communication protocols. The most vulnerable links in the shop floor are the resource constraint devices that communicate with minimum security standards. The integration of all those protocols must be handled properly and take in account all the gaps and vulnerabilities each one has. The cloud layer acts as a platform that can process and store all these big streams of data and at the same time being accessible from anywhere. In addition, it offers users the ability to use these data in services as predictive analytics or machine learning that can provide helpful insights for the industry’s current state and future. Alteration of these data can seriously harm an industry thus, the monitoring and secure authorization of connected users on the cloud is mandatory for a secure network. The factory layer or the API layer is used by humans to interact with the cloud and make use of the plethora of analytics services to implement favorable decisions for the industry. For
users to process these data they must be displayed as plain text in the services. Thus, the authorization and authenticity of users in this layer must be handled accordingly to prevent any alteration of data that will lead to false implementation on the shop floor line. Attacks in an IoT network have mainly two goals: stealing data or gaining control of a machine. Data control attacks aim mostly in altering or exfiltrating crucial information, which may be used for blackmailing [7]. Machine control attacks aim in gaining access to the system and remote-controlling actuators, sensors, robots and any connected machine. The horizon of both attacks spans from low level data leakage to high level attacks in which even human operators can be harmed [6]. The extent of an attack lies on the goal of the adversary. The actions of the attacker determine the outcome of the attack. Simple, low – level, extraction of data may not be crucial to the industry, but a medium level extraction of the private key will lead to a major violation of the integrity and privacy of the network and data [8]. One of the most unwanted scenarios would be an adversary masquerading a trustworthy party to implement falsified data to the shop-floor machines, exposing the operators and machines in grave danger. The potential cyber-attacks in a CPS are summed up in Fig. 2. The potential impact of each attack is also listed. An IoT architecture design must be security-driven to ensure the human-machine safety. An IoT security framework should adapt to existing resources and apply security mechanisms properly to every layer before providing any service to end users. Previously the first step to securing a network was the identification and monitoring of IoT devices [11]. Tracking the actions of IoT devices in a network is the simplest way of preventing unwanted scenarios, but as the system grow in scale, this become unmanageable. The next step is to analyze the network in its entity, through scanning and patching cycles, and map the vulnerable points by identifying potential breaches and weak links. The sensors, actuators and machine-to-machine terminals used in the shop-floor are usually low cost and resource-constrained components. Due to their low energy consumption and constrained computational power, they are designed with minimum security configurations to be able to adapt to the diverse monitoring scenarios and communication protocols. Thus, becoming an easy target for data and machine
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Fig. 2: An assessment of Cyber-attacks in a CPS (Estimation Factors: The estimation of impact levels was based on two main factors: Economic loss & Recovery period)
attacks. The reduction of the attack surface starts by designing and building resilient devices. 3.1. Proposed Solution After identifying threats and vulnerabilities, the next step is to analyse the configurations that could be implemented to secure the existing vulnerabilities. The communication protocols used mainly in the shop-floor for IoT applications are Bluetooth 5.0, IEEE 802.11, IEEE 802.15.4 and 6LoWPAN [22]. These protocols are suited for the shop-floor for they can be implemented in resource constrained sensors and actuators, expanding the diversity of IoT devices and transforming the shop-floor in a WSN. As far as the Bluetooth technology is concerned, the main problem is the security during pairing mode as it is based only on a shared pin that can be guessed or broken using brute force attacks, exfiltrated through a backdoor or by intercepting the session [13]. The solution proposed for this vulnerability is the implementation of a dual secret key that follows the philosophy of the public – private key security. For the IEEE 802.11/Wi-Fi the most reliable security mechanisms implemented are the WPA/WPA2 [19]. These mechanisms are considered secure since breaking the key with a brute force attack can last from seconds to weeks, depending on the key length and complexity. However, adversaries can extract the key length from the server and minimize the time needed to guess the key. The best ways to tackle this vulnerability is by hiding the SSID of the network, making it invisible to outsiders. In addition, an implementation of shell script code, allowing only a certain number of tries for a certain time of period can significantly increase the needed time. IEEE 802.15.4/Zigbee is a protocol used mainly to interconnect resource constrained devices at a short range and is designed with minimum or none security mechanisms to keep the devices low cost and highly compatible [21]. The security mechanism used in Zigbee is the link layer encryption using the AES 128 encryption algorithm. Although the AES encryption algorithm requires a lot of time to be broken there are other attacks that can be implemented. The power depletion attack is used to keep the Zigbee nodes awake in order to increase their power consumption and drain their battery. Other attacks as Distribute Denial of Service (DDoS) could jam the communication between nodes or flood the node’s network
with thousands of requests [21]. However, the main problem with Zigbee lies on the fact that when a new node is added, the shared key is transmitted wirelessly where it can be sniffed and obtained by a third party that will decrypt it using an AES decryptor. The solution proposed for the Zigbee protocol to tackle these vulnerabilities is a remote alert system for the power consumption of nodes and a key management service to monitor the user access. However, to prevent key sniffing the shared key must be pre-installed on the new nodes and not be transmitted over the air. Lastly, the 6LoWPAN was designed to combine the IPv6 protocol with the IEEE 802.15.4 protocol used by low computational devices [24]. Thus, it has the same vulnerabilities as the Zigbee protocol and the vulnerabilities the network, transport and application layers, of an IPv6 network, have. DoS/ DDoS attacks can flood the system with requests, injection of malicious code can reroute the messages to a different address and impersonating a trustworthy node can lead to flooding the network, sending echo messages as responds. 6LoWPAN requires the same security mechanisms as the Zigbee and an intrusion system to limit the network access to authorized users only while constantly monitoring the network traffic. 4. Case Study & Results In order to apply this mapping in a real use case in the shop floor, a data acquisition device (DAQ) was designed and created, which upgrades a production machinery into a Thing that can be connected in a WSN. The DAQ is connected with sensors and cameras that monitor the machine tools. First, a microcontroller (STM32F429) supported by a large community (Arduino) was selected to provide an API for the IEEE 802.15.4/Zigbee communications and monitoring. For the constructed system to communicate locally with the coordinator of the WSN and with wireless sensors in small ranges, an XBEE Zigbee module and a Bluetooth interface was installed. The selection of Zigbee over the other standards was made since it supports various topologies and encryption algorithms. The WSN is coordinated by a microcomputer (Raspberry Pi 2) with Linux responsible to collect all data from the network’s nodes in the shop floor. The data from every task are stored locally in a Structured Query Language (SQL) and
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Table 1: Attacks and Solutions for the vulnerabilities of IoT communication protocols Protocols
Security mechanisms [23] Secure Pairing
− − − −
Attacks Password guessing attack [13] DoS / DDoS attack Trojan/Backdoor attack MITM attacks
Bluetooth 5.0
IEEE 802.11/Wi-Fi
WEP, AES, WPA, WPA2
− − −
Password guessing attack [23] MITM attacks DoS/DDoS attack
IEEE 802.15.4/Zigbee
Link Layer encryption with AES-128, TLS
6LoWPAN
Link Layer encryption with AES-128, Access control list
− − − − − − − − −
Power depletion attack [21] MITM attacks Spoofing attack DoS/DDoS attack Sniffing attack [20] Power depletion attack DoS/DDoS/Jamming attack Transport Layer attack Spoofing attack
are transmitted through a REST service via HTTP to the main server located in the Cloud, integrated in a MongoDB NoSQL database, to store them. This IoT monitoring system is designed to support integration with existing industrial equipment, including the most potentially supportive architecture and standard for IoT, with more than 48% company members in Europe, the OPC – UA [5]. Thus, in our designed network, the microcomputer acts as an OPC – UA server. The microcontroller unit receives data from the machine sensors and stores them locally until the performed task is completed. Then, it propagates the stored data to the OPC – UA server, in the cloud layer, in order to be processed and accessed by users. The DAQ device and the WSN network were designed security driven to ensure the data integrity and user authentication. The power supply board for our purpose is an auxiliary board powered by a DC – DC converter. The selection of the power supply board was decided to avoid power depletion attacks aiming to drain its battery life. Moreover, the entire DAQ device is enclosed in a plastic transparent box to prevent any unauthorized physical intrusion. The transmitted data from the microcontroller to the DAQ device and to the Cloud database are encrypted using the Advance Encryption Standard (AES) algorithm with 128 bits. AES is an encryption algorithm introduced by NIST in 2001 to tackle with the vulnerabilities of previous algorithms [25]. It uses cryptographic keys of 128-bits to encrypt and decrypt data. The selection of a 128-bit key was made to prevent adversaries from guessing the key or use brute force attacks to identify the key, as it requires a lot of time and effort for a computer to do so and combined with the short range of Zigbee communication it proves a secure way of ensuring the data integrity. As far as the user identification is concerned the identity of every node is authorized through the Secure Socket Layer (SSL) and Transport Layer Security (TLS) protocols. TLS, and its predecessor SSL are used to secure the communication between the two parties and the authenticity of the transmitted data [26]. At the start of the connection, the two parties negotiate on a shared secret that will be used to generate the encryption key for the connection. The shared secret is secure and cannot be obtained by any attacker even if he was in the middle of the connection. After securing the connection the protocol can identify the identity of the two parties using the public – private key cryptography system. Moreover, for the
Solutions − −
Dual shared secret key [13] RSA or DES implementation
− − − −
Hiding SSID [23] Disabling Telnet/SSH services Honeypot countermeasure Implement Shell script code
− −
Remote alert system [21] Key management
− − −
Remote monitoring of energy [20] Key management system Intrusion Detection System
security of the packets, TLS attaches a Message Authentication Code (MAC) to every packet to prevent undetected loss or alteration of the transmitted data. Hence, TLS protocol provides a robust and secure way of ensuring the integrity of the data and the identities of every node. In addition, a secure database authentication system is added to increase the security of our network. To prevent any Distributed Denial of Service attacks a Virtual Private Network (VPN) is set [27]. By using VPN, the IP address of the network is masqueraded with an IP from an external server. Thus, in DDoS attacks, only the external server will be affected and become inaccessible, leaving the local network unharmed. Implementing these security mechanisms has transformed the proposed WSN network into a secure and robust system that is not affected by third parties and adversaries trying to steal or alter data.
Fig. 3: Designed IIoT network with security mechanisms implemented
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270 6 Table 2: Case Results Vulnerable Points DAQ Device
Attacks
Countermeasure
Battery Drain
Power supply board
Physical Intrusion Sniffing
Communication Layer
Identity Spoofing
Enclosed DAQ device Data encryption with AES-128 User authentication with SSL/TLS
(DAQ to Cloud)
MITM attacks DDoS Attack
VPN to hide the true IP
Cloud Database
Identity Spoofing
Database authentication system
5. Conclusions
Data authentication with MAC
The IoT transforms industries into complex systems, such as CPS, connecting sensors, machines and actuators that communicate and exchange data to provide a flexible and fully aware system. However, as wireless connections increased, it has given adversaries the ability to move from manipulating data to controlling actuators and machines. The expanded landscape of IoT provides more opportunities and methods for adversaries to exploit a network leading to numerous threats. For industries to adopt the Industry 4.0 philosophy these vulnerabilities must be addressed properly. This paper is an attempt to identify the existing vulnerabilities in an IoT landscape, assess the potential impact they can have on an industry’s human and machine resources and propose a robust way of securing a WSN. Our designed network was implemented in the shop-floor, as its devices are usually designed with a focus on cost and not so much on security, to highlight the importance and need of cybersecurity at every layer before providing a service to users. Future work will aim to connect different locations under the same system, including data connection between the involved parties, to map any new vulnerabilities that may arise in this complex case. Acknowledgement This work has been partially supported by the H2020 EC funded project “An Integrated Collaborative Platform for Managing the Product- Service Engineering LifecycleICP4Life (GA No: 636862). The authors would like to thank the industrial partner involved in this research. References [1] Mourtzis D., Vlachou E., Milas N. Industrial Big Data as a Result of IoT Adoption in Manufacturing. Procedia CIRP 2016. DOI: 10.1016/j.procir.2016.07.038 [2] IoT multiplies risk of attack. Network Security 2015. DOI: 10.1016/S13534858(15)30041-6 [3] O’Neill Mark. The Internet of Things: do more devices mean more risk?. Computer Fraud & Security 2014.DOI: 10.1016/S1361-3723(14)70008-9 [4] Mourtzis, D., Doukas, M., Bernidaki, D. “Simulation in manufacturing: Review and challenges”. Procedia CIRP, 25, 213-229, 2014 [5] OPC Foundation “OPC Unified Architecture: Interoperability for Industry 4.0 and the Internet of Things”, Report 2017. Available at: https://opcfoundation.org/wp-content/uploads/2016/05/OPC-UAInteroperability-For-Industrie4-and-IoT-EN-v5.pdf [6] Macaulay Tyson, Threats and Impacts to the IoT. Book Title: RIoT Control. Elsevier 2017, ISBN: 978-0-12-419971-2
[7] Mansfield-Devine Steve. Ransomware: the most popular form of attack. Computer Fraud & Security 2017.DOI: 10.1016/S1361-3723(17)30092-1 [8] Eldewahi A. E. W. et.al, SSL/TLS attacks: Analysis and evaluation. 2015 (ICCNEEE), DOI: 10.1109/ICCNEEE.2015.7381362 [9] Hiran Rahul, Carlsson Niklas, Shahmehri Nahid. Collaborative framework for protection against attacks targeting BGP and edge networks. Computer Networks 2017. DOI: 10.1016/j.comnet.2017.04.048 [10] National Institute of Standards and Technology ‘Guide for Conducting Risk Assessments’, 2012, https://dx.doi.org/10.6028/NIST.SP.800-30r1 [11] Bajpai Pranshu et al., The art of mapping IoT devices in networks. Network Security 2018. DOI: 10.1016/S1353-4858(18)30033-3 [12] Moon Jaegeun, et al. IoT application protection against power analysis attack. 2018. DOI: 10.1016/j.compeleceng.2018.02.030 [13] Vilmalesh Kumar Dubey et al., A review on Bluetooth Security Vulnerabilitites and a Proposed Prototype Model for Enchancing Security against MITM attack. (IJRSCSE) 2015, ISSN: 2349-4859 [14] Vayansky Ike, Kumar Sathish. Phishing–challenges and solutions. Computer Fraud & Security 2018.DOI: 10.1016/S1361-3723(18)30007-1 [15] Ahmed Ejaz et al. The rise of ransomware and emerging security challenges in the Internet of Things. Journal Article in Computer Networks 2017. DOI: 10.1016/j.comnet.2017.09.003 [16] Dooley Michael, Rooney Timothy. DNS security management. WileyIEEE Press 2017. ISBN: 978-1-119-32827-8 [17] Carlin Andrew, Hammoudeh Mohammad, Aldabbas Omar. Defence for Distributed Denial of Service Attacks in Cloud Computing. Procedia Computer Science 2015. DOI: 10.1016/j.procs.2015.12.037 [18] A. Aarthy Devi, Mohan Ashok Kumar, M Sethumadhavan. Wireless Security Auditing: Attack Vectors and Mitigation Strategies. Procedia Computer Science 2017. DOI: 10.1016/j.procs.2017.09.153 [19] Tsitroulis Achilleas et al., Exposing WPA2 security protocol vulnerabilities. IJICS Conference 2014. DOI: 10.1504/IJICS.2014.059797 [20] Adat Vipindev, Gupta B. B.. Security in Internet of Things: issues, challenges, taxonomy, and architecture. Telecommunication Systems by Springer 2018, p. 423-441. DOI: 10.1007/s11235-017-0345-9 [21] Vidgren Niko, et al., Security Threats in ZigBee-Enabled Systems: Vulnerability Evaluation, Practical Experiments, Countermeasures, and Lessons Learned. 2013 46 th HICSS. DOI: 10.1109/HICSS.2013.475 [22] Mourtzis, D., Milas, N., & Vlachou, A. (2018). An Internet of ThingsBased Monitoring System for Shop-Floor Control. Journal of Computing and Information Science in Engineering, 18(2), 021005. [23] Mekhaznia Tahar, Zidani Abdelmadjid. Wi-Fi Security Analysis. Procedia Computer Science 2015. DOI: 10.1016/j.procs.2015.12.009 [24] Internet Engineering Task Force (IETF) ‘IPv6 over Low-Power Wireless Personal Area Network (6LoWPAN), https://tools.ietf.org/html/rfc8138 [25] National Institute of Standards and Technology (NIST) ‘Announcing the Advanced Encryption Standard (AES)’, 2001, https://nvlpubs.nist.gov/nistpubs/fips/nist.fips.197.pdf [26] Internet Engineering Task Force (IETF) ‘ The Transport Layer Security Protocol (TLS) Protocol Version 1.3’, ISSN: 2070-1721, https://tools.ietf.org/html/rfc8446 [27] Rahimi Sanaz, Zargham Mehdi. Analysis of the security of VPN configurations in industrial control environments. International Journal of Critical Infrastructure Protection 2012. DOI: 10.1016/j.ijcip.2012.01.001 [28] Wright Marie A, A look at public key certificates, Network Security 1998, DOI: 10.1016/S1353-4858(97)83135-2 [29] MarketsAndMarkets Ltd., Industrial Cybersecurity Market by Type & Region - Global Forecast 2023, 2017 Report, available at: https://www.marketsandmarkets.com/Market-Reports/industrialcybersecurity-market-37646764.html [30] Kaspersky ICS, The state of Industrial Cybersecurity 2017 Report, available at: https://ics.kaspersky.com/the-state-of-industrialcybersecurity-2018/ [31] Ponemon Larry, 13th annual Cost of a Data Breach study, IBM Report 2018, available at: https://www.ibm.com/security/data-breach [32] Deloitte, Industry 4.0 and Cybersecurity, 2017 , available at: https://www2.deloitte.com/au/en/pages/risk/articles/industry-4-cybersecurity.html [33] Ponemon Institute, Cost of Cybercrime Study, Accenture Study 2017, available at: https://www.accenture.com/us-en/insight-cost-ofcybercrime-2017?src=SOMS
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29th CIRP Design 2019 (CIRP Design 2019) 29th CIRP Design 2019 (CIRP Design 2019)
Mapping in Industrial Internet of Things Mapping Vulnerabilities Vulnerabilities in the the Industrial Internet ofFrance Things Landscape Landscape 28th CIRP Design Conference, May 2018, Nantes, Dimitris Mourtzisa*, Konstantinos Angelopoulosa, Vasilios Zogopoulosa
a a a Dimitris Mourtzis *, Konstantinos Angelopoulosand , Vasilios Zogopoulos A new methodology to analyze the functional physical architecture of Laboratory for Manufacturing Systems and Automation (LMS), Department of Mechanical Engineering and Aeronautics, University o f Patras, Laboratory for Manufacturing Systems and Automation (LMS), of Mechanical Engineering and Aeronautics, University o f Patras, RioDepartment Patras, Greece existing products for an assembly oriented product family identification Rio Patras, Greece a a
Paul Stief *, Jean-Yves Dantan, Alain Etienne, Ali Siadat
* Corresponding author. Tel.: +30-2610-910160; fax: +30-2610-997314. E-mail address: [email protected] * Corresponding author. Tel.: +30-2610-910160; fax: +30-2610-997314. E-mail address: [email protected]
École Nationale Supérieure d’Arts et Métiers, Arts et Métiers ParisTech, LCFC EA 4495, 4 Rue Augustin Fresnel, Metz 57078, France
Abstract
*Abstract Corresponding author. Tel.: +33 3 87 37 54 30; E-mail address: [email protected]
With the advent of the fourth industrial revolution manufacturing systems are transformed into digital ecosystems via internet communication With the advent of the revolution are transformed intoamount, digital variety ecosystems via internetofcommunication technologies to form thefourth smart industrial factories of the future.manufacturing Industry 4.0 issystems characterized by increased and complexity the exchanged technologies to form the smart factories of the future. Industry 4.0 is characterized by increased amount, variety and complexity of the exchanged data. Industries utilize a plethora of machines, robots, computers and servers, all connected to each other through different networks. Between Abstract data. Industries a plethora of machines, computers and servers, connected to each other different networks. Between those machines, utilize a massive exchange of data androbots, sensitive information is takingall place. Whenever there is a through new interface between networks and those machines, exchange of dataasand sensitive information takingdevices place. enter Whenever thereofisInternet a new interface between devices, it poses aa massive weak link and is marked potential attack point. Asismore the realm of Things, attacksnetworks aimed atand the Indevices, today’s business environment, themarked trend towards moreattack product variety and customization is unbroken. Due to of this development, the need of poses a weak link and as potential point. As more devices enter the of Internet attacksbreaches aimed atthat the variety ofit Internet-endpoints will isgrow. However, existing internet technologies are plagued byrealm data privacy issues Things, and malicious agile andofreconfigurable production systems emerged to cope with technologies various products and product families. To design and optimizebreaches production variety Internet-endpoints will grow. However, existing internet are plagued by data privacy issues and malicious that will act as a setback for adopters of Industry 4.0 technologies. Thus, the need for identifying the required cybersecurity protocols, at specific systems well as to for choose the optimal product matches, product analysis methods are needed. Indeed, most of the known methods aim to will act as as where a setback adopters of Industry 4.0 technologies. Thus, the need for identifying the The required cybersecurity protocols, specific endpoints data leaks may occur, has been vastly increased to tackle malicious behaviors. true potential of Industry 4.0 at cannot be analyze a product or oneleaks product family on thebeen physical level. Different product families,behaviors. however, may differ largely inofterms of the4.0 number and endpoints where data may occur, has vastly increased to tackle malicious The true potential Industry cannot be achieved, if those challenges are not addressed properly. This work aims to identify and map the potential vulnerable endpoints in a common nature of components. This fact impedes an efficient comparison and choice of appropriate product familyvulnerable combinations for thein production achieved, if those challenges are not addressed properly. This work aims to identify and map the potential endpoints a common industrial paradigm, where data will cross during aggregation and propose a robust way of securing a wireless sensor network (WSN) by ensuring system. A new methodology is proposed toduring analyze existing products in view of their functional and physicalsensor architecture. The aim is cluster industrial paradigm, where data aggregation and a robust waythe of WSN. securing a wireless network bytoensuring the integrity and authenticity ofwill the cross parcels and the identity of thepropose users comprising The vulnerabilities of a data(WSN) acquisition system these productsand in new assemblyoforiented product for the optimization of existing lines and the creation future reconfigurable the integrity authenticity the parcels and families the of the users comprising theassembly WSN. The vulnerabilities of a of data acquisition system applied in the laser machine industry are mapped andidentity presented. assembly on Datum Chain, the structure of the products is analyzed. Functional subassemblies are identified, and applied insystems. the laserBased machine industryFlow are mapped andphysical presented. a functional analysis is performed. Moreover, a hybrid functional and physical architecture graph (HyFPAG) is the output which depicts the © Authors. Published by B.V. © 2019 2019 The The Authors. Published by Elsevier Elsevier B.V. similarity between product families by providing design support to both, production system planners and product designers. An illustrative © 2019 The Authors. Published by Elsevier B.V. committee Peer-review under responsibility of the scientific of Design Conference Conference 2019. 2019 Peer-review under responsibility of the scientific committee of the the CIRP CIRP Design example of a under nail-clipper is used to the proposed methodology. industrial case study on two product families of steering columns of Peer-review responsibility ofexplain the scientific committee of the CIRPAn Design Conference 2019 thyssenkrupp Presta France is then carried out to give a first industrial evaluation of the proposed approach. Keywords: Cybersecurity; IoT; Privacy; Wireless Sensor Networks ©Keywords: 2017 TheCybersecurity; Authors. Published by Elsevier IoT; Privacy; WirelessB.V. Sensor Networks Peer-review under responsibility of the scientific committee of the 28th CIRP Design Conference 2018.
devices to collaborate and exchange data. devices to collaborate and landscape exchange data. However, the diverse of Industry 4.0 leads to However, the diverse landscape Industry to multiple interfaces between networksofand devices4.0to leads support The fourth Industrial revolution has a vision of intelligent multiple interfaces between networks and devices to support The fourth Industrial revolution has a vision of intelligent the different sensors, devices and machines. Each interface and and smart factories, allowing smart systems with autonomous the different deviceshas anddifferent machines. Each interface and smart factories, allowing smart systems with autonomous link betweensensors, the networks vulnerabilities andand is properties to guide manufacturing ecosystems as data 1.properties Introduction of thebetween productthe range and characteristics manufactured and/or link networks has different vulnerabilities and is to guide manufacturing ecosystems as data susceptible to different attacks. With the adoption of Industry monitoring and saving, self-healing and self-configuring. Its assembled intothis system. In this With context, main challenge in susceptible different thethe adoption of Industry monitoring saving,from self-healing andofself-configuring. Its 4.0 the number of theseattacks. links will increase, providing more true potentialand emanates the plethora devices, or Things, Due to the fast development in the domain of modelling and analysis is now not only to cope with single 4.0 the number of these links will increase, providing more true potential emanates from the plethora of devices,oforeveryday Things, attack points to adversaries [2,3] and since existing internet communicating with each other. The introduction communication and each an ongoing trend of digitization and products, a limited product range existing product families, attack points to plagued adversaries [2,3] and since existing internet communicating with other. The introduction of everyday technologies are with dataor privacy issues and breaches, devices to the variety of communication technologies created digitalization, manufacturing enterprises are facing important but also to be able to analyze and to compare products to technologies are plagued with data privacy issues and breaches, devices to the variety of communication technologies created the true potential of Industry 4.0 will not be achieved ifdefine these the realm of Internet of Things (IoT), a core segment of the new the true potential Industry 4.0 will notthat be classical achieved existing if these challenges in today’s market environments: a continuing new product families. It can be observed the realm of Internet of Things (IoT), a core segment of the new challenges are notof addressed properly. Industry 4.0 era. This resulted in the generation of massive challenges areaims not tendency reduction oftoproduct times and product areaddressed regrouped in function of clients or features. Industry 4.0data era.that Thisneeded resulted in propagated the development generation massive This families paper to map theproperly. vulnerabilities in an Industry 4.0 amounts towards of be and of stored, for This paper aims to map the vulnerabilities in an Industry 4.0 amounts of data that needed to be propagated and stored, for shortened product lifecycles. In addition, there is an increasing However, assembly oriented product families are hardly to find. network paradigm and list potential attacks that can be utilized the smart systems and services to harness them [1]. network paradigm and list potential attacks that can be utilized theAnother smart and services to harness them [1]. demand ofsystems customization, being at the same time in a global On the product family level, products differ mainly in two core feature of Industry 4.0 is the communication to disrupt the networks integrity. Moreover, several standards Anotherdifferent core of Industry 4.0the isthe the communication to disrupt the networks integrity. Moreover, several standards competition with feature competitors all over world. This trend, main characteristics: (i) the number ofintegrations components and (ii) the between companies and promotion of and protocols that may support the are listed. A between different the macro promotion of and protocols that may support the integrations are listed. A which is inducing thecompanies development to micro type of components (e.g. mechanical, electrical, electronical). collaborative scenarios. This featureand isfrom enabled by integrating paradigm of an IoT system in production is used, to depict how collaborative scenarios. feature is enabled byaugmenting integrating paradigm of an IoT system in production is used, to depict how markets, results inin diminished lot networks, sizes due to Classical methodologies considering mainly single products cloud platforms the This Industrial which provide the obstacles have been overcome. cloud platforms inand thea Industrial networks, whichand provide thesolitary, obstaclesalready have been overcome. product varieties (high-volume low-volume production) [1]. or existing product families analyze the databases, services securetospace for employees databases, services and a securevariety space as forwell employees To cope with this augmenting as to beand able to product structure on a physical level (components level) which identify in the existing causes difficulties regarding an efficient definition and 2212-8271 possible © 2019 The optimization Authors. Publishedpotentials by Elsevier B.V. 2212-8271 2019responsibility The Authors. of Published Elsevier B.V.of the CIRP Design Conference 2019 Peer-review©under the scientific committee production system, it is important tobyhave a precise knowledge comparison of different product families. Addressing this 1. Introduction
Keywords: Assembly; Design method; Family identification 1. Introduction
Peer-review under responsibility of the scientific committee of the CIRP Design Conference 2019
2212-8271©©2017 2019The The Authors. Published by Elsevier 2212-8271 Authors. Published by Elsevier B.V. B.V. Peer-review under responsibility of scientific the scientific committee theCIRP CIRP Design Conference 2019. Peer-review under responsibility of the committee of the of 28th Design Conference 2018. 10.1016/j.procir.2019.04.201
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2. State of the Art Advances in manufacturing and communication technologies set the main foundations for transforming current industries into Cyber-Physical Systems (CPS) via the integration of IoT. However, the adoption of IoT in manufacturing results in the generation of massive streams of data that must be propagated in order to be stored and processed [1]. For these data to be used they must be propagated through an API to a cloud platform as plain text, so users can interpret them. The variety of data requires many APIs located in a cloud platform or at the local network. However, these interfaces pose weak links that adversaries could potentially exploit [2]. The foundation of Industry 4.0 is based on the Internet of Things, a network of interconnected devices that create a smart grid. The IoT concept started to affect manufacturing when the cost for its implementation reduced and the various communication technologies evolved rapidly. This combination enabled all sort of devices to be connected and exchange various information. The appliance of IoT in industry includes sensors, actuators, robots, machine-to-machine communications (M2M), Cloud platforms as well as security mechanisms to ensure a secure and reliable exchange of data. In previous years the shop-floor was comprised only by machines that communicated with a single computer via physical connection and exchange data without the risk of adversaries being able to intercept the connection. However, with the introduction of IoT, modern industries are transformed into Cyber Physical Systems (CPS) comprised of numerous interconnected devices and sensors. In the CPS model the main source of generated data is the shop-floor. Nowadays shop-floors are filled with devices embedded with communication technologies that transmit data using various protocols and form a wireless sensor network (WSN). The data from these devices are very valuable and can be used with various services to provide insights for the future of the company and for upgrading the overall shop-floor. This kind of data is distributed both locally and remotely, potentially to users outside the company. Due to the bulk of those data, a propagation must occur first to assemble all data in a storage, in order to be distinguished in two categories: structured and unstructured data. Structured data have distinct length and can be used directly for analysis and prediction models, unlike unstructured data that needs to be processed first to obtain any helpful information. The solution for this pool of data lies on cloud technology. The Cloud layer is one of the pillars of IoT which can provide a storage platform that can gather data from all kind of sources and simultaneously be accessed by any device via internet. The advantage a cloud platform gives to an Industry is the construction of a digital identical industry that will be fed with real time data and run simulations on future predictions, to prevent any unwanted scenarios and suggest modifications and optimization techniques to upgrade the overall performance [4]. These helpful insights are used by employees with access to the cloud layer. The complex character of IoT revolutionized the modern Industry by transforming it to a complex intercommunicating system. For Industries to adopt the IoT, new protocols and architectures must be implemented to monitor, collect and regulate this pool of data. In manufacturing the most reliable method for communicating is setting up a Wireless Sensor
Network (WSN), connecting many sensor devices collaborating to achieve the same goal. The complexity of IoT requires an architecture that can be flexible and adapt to the variety of devices and communication protocols. Numerous protocols have been developed, with the most complete and adopted being the OPC – Unified Architecture (OPC – UA) [5]. The OPC – UA allows servers to provide real time processed and non-processed data to all clients. OPC – UA interoperability enables other systems not familiar with the data model to browse through the server and gather information. The OPC - UA aims to provide interoperability among the diversity of devices and ensure the authority of the users and the authenticity of the exchanged data. In addition, many security mechanisms have been constructed to prevent adversaries from accessing stolen data by encrypting the messages [5]. OPC – UA mainly uses the Advanced Encryption Standard (AES) [25] and the X.509 standard [28] that have proven to be robust security mechanisms for message encryption and integrity. However, for employees to use and understand the data they must be propagated through an application programming interface (API) to a database to be processed, categorized and displayed as plain text. APIs are the endpoint of communication between the various connected devices that comprise the WSN. An API has multiple connections with various devices simultaneously and can receive and sent data from the shopfloor to the factory level and vice versa. It monitors the devices comprising the shop-floor and regulates the flow of data. This aggregation of data at single endpoints poses a lucrative link for adversaries. According to a recent survey by Deloitte 70% of manufacturers transmit personal information through connected devices while only 55% of that information is encrypted [32]. Thus, the misusage of API can be catastrophically for industries leading from stealing/altering data to the death of human resources [6]. Although, the diversity of connected devices and communication protocols is beneficial for industries, it provides an expanded surface of attack points for adversaries [3]. According to the National Institute of Standards and Technology (NIST) guide for conducting Risk Assessments in IT systems, a vulnerability is defined as a ‘flaw or weakness in system security procedure, design, implementation, or internal controls that could result in a security breach or a violation of systems security policy’ [10]. Each protocol has its own different vulnerabilities thus, giving attackers more than own way of intercepting sessions and tampering with data. The introduction of IoT has given the ability to third parties to switch from manipulating data to controlling actuators and machines. The vulnerabilities of IoT networks are the key points of securing IoT in its entity and subsequently transform Industry 4.0 into a more approachable scenario. An industry must be resilient when attacked, minimizing the effects of the attack and restoring security as quickly as possible [32]. Evidence for the necessity of cybersecurity at all layers are in the numbers. According to a recent report by Markets and Markets the Global Industrial cybersecurity market will reach $22.79 billion by 2023 [29], while the average annual loss from cyberattacks for industries ranges from $347,000 to $497,000
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Fig. 1: A typical IoT network
depending on the industry’s size [30]. An Accenture study has shown that the largest cost component from a cyberattack is information loss, accounting for up to 43% of the cost [33]. In addition, the 13th annual report by IBM refers to the influence of IoT devices and AI to data-breach costs [31]. Thus, the need for cybersecurity is of most importance for a healthy transition to the Industry 4.0 realm. In order to secure a network, one needs to map and identify the various vulnerabilities each different link has and apply the most prominent technologies to prevent any unwanted scenario. 3. System Mapping This paper presents an approach for mapping possible weak links in an IoT network based on the vulnerabilities and gaps each industrial communication technology and protocol possess. A typical scenario of interconnected devices in an IoT network can be seen in Fig. 1. The network consists of three main layers: The Shop-floor, the Cloud layer and the Factory layer. The shop floor is the main source of data and consists mainly of sensors, actuators, robots, machines and IoT nodes that generate streams of data. These streams of data must be propagated to the cloud layer or to a DAQ service. The variety of devices on the shop floor implies the integration of a variety of communication protocols. The most vulnerable links in the shop floor are the resource constraint devices that communicate with minimum security standards. The integration of all those protocols must be handled properly and take in account all the gaps and vulnerabilities each one has. The cloud layer acts as a platform that can process and store all these big streams of data and at the same time being accessible from anywhere. In addition, it offers users the ability to use these data in services as predictive analytics or machine learning that can provide helpful insights for the industry’s current state and future. Alteration of these data can seriously harm an industry thus, the monitoring and secure authorization of connected users on the cloud is mandatory for a secure network. The factory layer or the API layer is used by humans to interact with the cloud and make use of the plethora of analytics services to implement favorable decisions for the industry. For
users to process these data they must be displayed as plain text in the services. Thus, the authorization and authenticity of users in this layer must be handled accordingly to prevent any alteration of data that will lead to false implementation on the shop floor line. Attacks in an IoT network have mainly two goals: stealing data or gaining control of a machine. Data control attacks aim mostly in altering or exfiltrating crucial information, which may be used for blackmailing [7]. Machine control attacks aim in gaining access to the system and remote-controlling actuators, sensors, robots and any connected machine. The horizon of both attacks spans from low level data leakage to high level attacks in which even human operators can be harmed [6]. The extent of an attack lies on the goal of the adversary. The actions of the attacker determine the outcome of the attack. Simple, low – level, extraction of data may not be crucial to the industry, but a medium level extraction of the private key will lead to a major violation of the integrity and privacy of the network and data [8]. One of the most unwanted scenarios would be an adversary masquerading a trustworthy party to implement falsified data to the shop-floor machines, exposing the operators and machines in grave danger. The potential cyber-attacks in a CPS are summed up in Fig. 2. The potential impact of each attack is also listed. An IoT architecture design must be security-driven to ensure the human-machine safety. An IoT security framework should adapt to existing resources and apply security mechanisms properly to every layer before providing any service to end users. Previously the first step to securing a network was the identification and monitoring of IoT devices [11]. Tracking the actions of IoT devices in a network is the simplest way of preventing unwanted scenarios, but as the system grow in scale, this become unmanageable. The next step is to analyze the network in its entity, through scanning and patching cycles, and map the vulnerable points by identifying potential breaches and weak links. The sensors, actuators and machine-to-machine terminals used in the shop-floor are usually low cost and resource-constrained components. Due to their low energy consumption and constrained computational power, they are designed with minimum security configurations to be able to adapt to the diverse monitoring scenarios and communication protocols. Thus, becoming an easy target for data and machine
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Fig. 2: An assessment of Cyber-attacks in a CPS (Estimation Factors: The estimation of impact levels was based on two main factors: Economic loss & Recovery period)
attacks. The reduction of the attack surface starts by designing and building resilient devices. 3.1. Proposed Solution After identifying threats and vulnerabilities, the next step is to analyse the configurations that could be implemented to secure the existing vulnerabilities. The communication protocols used mainly in the shop-floor for IoT applications are Bluetooth 5.0, IEEE 802.11, IEEE 802.15.4 and 6LoWPAN [22]. These protocols are suited for the shop-floor for they can be implemented in resource constrained sensors and actuators, expanding the diversity of IoT devices and transforming the shop-floor in a WSN. As far as the Bluetooth technology is concerned, the main problem is the security during pairing mode as it is based only on a shared pin that can be guessed or broken using brute force attacks, exfiltrated through a backdoor or by intercepting the session [13]. The solution proposed for this vulnerability is the implementation of a dual secret key that follows the philosophy of the public – private key security. For the IEEE 802.11/Wi-Fi the most reliable security mechanisms implemented are the WPA/WPA2 [19]. These mechanisms are considered secure since breaking the key with a brute force attack can last from seconds to weeks, depending on the key length and complexity. However, adversaries can extract the key length from the server and minimize the time needed to guess the key. The best ways to tackle this vulnerability is by hiding the SSID of the network, making it invisible to outsiders. In addition, an implementation of shell script code, allowing only a certain number of tries for a certain time of period can significantly increase the needed time. IEEE 802.15.4/Zigbee is a protocol used mainly to interconnect resource constrained devices at a short range and is designed with minimum or none security mechanisms to keep the devices low cost and highly compatible [21]. The security mechanism used in Zigbee is the link layer encryption using the AES 128 encryption algorithm. Although the AES encryption algorithm requires a lot of time to be broken there are other attacks that can be implemented. The power depletion attack is used to keep the Zigbee nodes awake in order to increase their power consumption and drain their battery. Other attacks as Distribute Denial of Service (DDoS) could jam the communication between nodes or flood the node’s network
with thousands of requests [21]. However, the main problem with Zigbee lies on the fact that when a new node is added, the shared key is transmitted wirelessly where it can be sniffed and obtained by a third party that will decrypt it using an AES decryptor. The solution proposed for the Zigbee protocol to tackle these vulnerabilities is a remote alert system for the power consumption of nodes and a key management service to monitor the user access. However, to prevent key sniffing the shared key must be pre-installed on the new nodes and not be transmitted over the air. Lastly, the 6LoWPAN was designed to combine the IPv6 protocol with the IEEE 802.15.4 protocol used by low computational devices [24]. Thus, it has the same vulnerabilities as the Zigbee protocol and the vulnerabilities the network, transport and application layers, of an IPv6 network, have. DoS/ DDoS attacks can flood the system with requests, injection of malicious code can reroute the messages to a different address and impersonating a trustworthy node can lead to flooding the network, sending echo messages as responds. 6LoWPAN requires the same security mechanisms as the Zigbee and an intrusion system to limit the network access to authorized users only while constantly monitoring the network traffic. 4. Case Study & Results In order to apply this mapping in a real use case in the shop floor, a data acquisition device (DAQ) was designed and created, which upgrades a production machinery into a Thing that can be connected in a WSN. The DAQ is connected with sensors and cameras that monitor the machine tools. First, a microcontroller (STM32F429) supported by a large community (Arduino) was selected to provide an API for the IEEE 802.15.4/Zigbee communications and monitoring. For the constructed system to communicate locally with the coordinator of the WSN and with wireless sensors in small ranges, an XBEE Zigbee module and a Bluetooth interface was installed. The selection of Zigbee over the other standards was made since it supports various topologies and encryption algorithms. The WSN is coordinated by a microcomputer (Raspberry Pi 2) with Linux responsible to collect all data from the network’s nodes in the shop floor. The data from every task are stored locally in a Structured Query Language (SQL) and
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Table 1: Attacks and Solutions for the vulnerabilities of IoT communication protocols Protocols
Security mechanisms [23] Secure Pairing
− − − −
Attacks Password guessing attack [13] DoS / DDoS attack Trojan/Backdoor attack MITM attacks
Bluetooth 5.0
IEEE 802.11/Wi-Fi
WEP, AES, WPA, WPA2
− − −
Password guessing attack [23] MITM attacks DoS/DDoS attack
IEEE 802.15.4/Zigbee
Link Layer encryption with AES-128, TLS
6LoWPAN
Link Layer encryption with AES-128, Access control list
− − − − − − − − −
Power depletion attack [21] MITM attacks Spoofing attack DoS/DDoS attack Sniffing attack [20] Power depletion attack DoS/DDoS/Jamming attack Transport Layer attack Spoofing attack
are transmitted through a REST service via HTTP to the main server located in the Cloud, integrated in a MongoDB NoSQL database, to store them. This IoT monitoring system is designed to support integration with existing industrial equipment, including the most potentially supportive architecture and standard for IoT, with more than 48% company members in Europe, the OPC – UA [5]. Thus, in our designed network, the microcomputer acts as an OPC – UA server. The microcontroller unit receives data from the machine sensors and stores them locally until the performed task is completed. Then, it propagates the stored data to the OPC – UA server, in the cloud layer, in order to be processed and accessed by users. The DAQ device and the WSN network were designed security driven to ensure the data integrity and user authentication. The power supply board for our purpose is an auxiliary board powered by a DC – DC converter. The selection of the power supply board was decided to avoid power depletion attacks aiming to drain its battery life. Moreover, the entire DAQ device is enclosed in a plastic transparent box to prevent any unauthorized physical intrusion. The transmitted data from the microcontroller to the DAQ device and to the Cloud database are encrypted using the Advance Encryption Standard (AES) algorithm with 128 bits. AES is an encryption algorithm introduced by NIST in 2001 to tackle with the vulnerabilities of previous algorithms [25]. It uses cryptographic keys of 128-bits to encrypt and decrypt data. The selection of a 128-bit key was made to prevent adversaries from guessing the key or use brute force attacks to identify the key, as it requires a lot of time and effort for a computer to do so and combined with the short range of Zigbee communication it proves a secure way of ensuring the data integrity. As far as the user identification is concerned the identity of every node is authorized through the Secure Socket Layer (SSL) and Transport Layer Security (TLS) protocols. TLS, and its predecessor SSL are used to secure the communication between the two parties and the authenticity of the transmitted data [26]. At the start of the connection, the two parties negotiate on a shared secret that will be used to generate the encryption key for the connection. The shared secret is secure and cannot be obtained by any attacker even if he was in the middle of the connection. After securing the connection the protocol can identify the identity of the two parties using the public – private key cryptography system. Moreover, for the
Solutions − −
Dual shared secret key [13] RSA or DES implementation
− − − −
Hiding SSID [23] Disabling Telnet/SSH services Honeypot countermeasure Implement Shell script code
− −
Remote alert system [21] Key management
− − −
Remote monitoring of energy [20] Key management system Intrusion Detection System
security of the packets, TLS attaches a Message Authentication Code (MAC) to every packet to prevent undetected loss or alteration of the transmitted data. Hence, TLS protocol provides a robust and secure way of ensuring the integrity of the data and the identities of every node. In addition, a secure database authentication system is added to increase the security of our network. To prevent any Distributed Denial of Service attacks a Virtual Private Network (VPN) is set [27]. By using VPN, the IP address of the network is masqueraded with an IP from an external server. Thus, in DDoS attacks, only the external server will be affected and become inaccessible, leaving the local network unharmed. Implementing these security mechanisms has transformed the proposed WSN network into a secure and robust system that is not affected by third parties and adversaries trying to steal or alter data.
Fig. 3: Designed IIoT network with security mechanisms implemented
Dimitris Mourtzis et al. / Procedia CIRP 84 (2019) 265–270 Author name / Procedia CIRP 00 (2019) 000–000
270 6 Table 2: Case Results Vulnerable Points DAQ Device
Attacks
Countermeasure
Battery Drain
Power supply board
Physical Intrusion Sniffing
Communication Layer
Identity Spoofing
Enclosed DAQ device Data encryption with AES-128 User authentication with SSL/TLS
(DAQ to Cloud)
MITM attacks DDoS Attack
VPN to hide the true IP
Cloud Database
Identity Spoofing
Database authentication system
5. Conclusions
Data authentication with MAC
The IoT transforms industries into complex systems, such as CPS, connecting sensors, machines and actuators that communicate and exchange data to provide a flexible and fully aware system. However, as wireless connections increased, it has given adversaries the ability to move from manipulating data to controlling actuators and machines. The expanded landscape of IoT provides more opportunities and methods for adversaries to exploit a network leading to numerous threats. For industries to adopt the Industry 4.0 philosophy these vulnerabilities must be addressed properly. This paper is an attempt to identify the existing vulnerabilities in an IoT landscape, assess the potential impact they can have on an industry’s human and machine resources and propose a robust way of securing a WSN. Our designed network was implemented in the shop-floor, as its devices are usually designed with a focus on cost and not so much on security, to highlight the importance and need of cybersecurity at every layer before providing a service to users. Future work will aim to connect different locations under the same system, including data connection between the involved parties, to map any new vulnerabilities that may arise in this complex case. Acknowledgement This work has been partially supported by the H2020 EC funded project “An Integrated Collaborative Platform for Managing the Product- Service Engineering LifecycleICP4Life (GA No: 636862). The authors would like to thank the industrial partner involved in this research. References [1] Mourtzis D., Vlachou E., Milas N. Industrial Big Data as a Result of IoT Adoption in Manufacturing. Procedia CIRP 2016. DOI: 10.1016/j.procir.2016.07.038 [2] IoT multiplies risk of attack. Network Security 2015. DOI: 10.1016/S13534858(15)30041-6 [3] O’Neill Mark. The Internet of Things: do more devices mean more risk?. Computer Fraud & Security 2014.DOI: 10.1016/S1361-3723(14)70008-9 [4] Mourtzis, D., Doukas, M., Bernidaki, D. “Simulation in manufacturing: Review and challenges”. Procedia CIRP, 25, 213-229, 2014 [5] OPC Foundation “OPC Unified Architecture: Interoperability for Industry 4.0 and the Internet of Things”, Report 2017. Available at: https://opcfoundation.org/wp-content/uploads/2016/05/OPC-UAInteroperability-For-Industrie4-and-IoT-EN-v5.pdf [6] Macaulay Tyson, Threats and Impacts to the IoT. Book Title: RIoT Control. Elsevier 2017, ISBN: 978-0-12-419971-2
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