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Protection on Wireless Sensor Network from Clone Attack using the SDN-Enabled Hybrid Clone Node Detection Mechanisms
Computer Communications 152 (2020) 316–322
Contents lists available at ScienceDirect
Computer Communications journal homepage: www.elsevier.com/locate/comcom
Protection on Wireless Sensor Network from Clone Attack using the SDN-Enabled Hybrid Clone Node Detection Mechanisms P.P. Devi a ,∗, B. Jaison b a b
Aalim Muhammed Salegh College of Engineering, India R.M.K Engineering College, India
ARTICLE
INFO
Keywords: Clone attack Performance analysis Hybrid clone node detection Key distribution Wireless sensor network SDN QoS
ABSTRACT WSN is an infrastructure less network that consists of mobile nodes that communicate with each other over wireless links. WSN is vulnerable to the node replication attack (clone attack). Attackers through compromising one sensor node replicate many clones having the same identity (ID) from the compromised node, and place these clones in various places of network. Clones contain all the credentials of legitimate member so appears authentic. This makes the conventional cryptographic tools useless and clone detection difficult. Once the node replication attack has been successful it can help the attacker to exploit almost all of the network operations, like routing, data collection, and key distribution, and also to help launch various other attacks such as black hole, wormhole etc. This proposed work therefore attempts a SDN based mechanism that implements a network level route analysis and time-based analysis methods which involves a low cost timely monitoring of the environment to identify and avoid redundant nodes which may be caused due to cloning attack. Thus, the SDN based cyber security applications are most useful in this situation. The implementation of this SDN based mechanism in WSN helps in maintaining and improving the QoS (Quality of service) constraints. The hybrid clone node detection (HCND) mechanism helps to detect the clone node present in the wireless network. This is to perform efficient clone detection in such a way to eliminate cloning attack in proactive fashion. To detect clones locally as well as across geographical region through cost effective identity verification procedure. This method helps to protect the wireless sensor network from the node identity replicas using the superimposed SDIS junction code. The node identity replicas help to choose the credible path for successful transmissions. The superimposed method is to be used for retrieval of information from node participating on the network. To thwart cluster of attacks hosted from the clones, by removing the hosting clones. The simulation result shows that there is the performance analysis of various parameters such as false positive, false negative ratio analysis, precision analysis, recall analysis and detection analysis.
1. Introduction The wireless sensor network is defined as a network of devices which is to communicate the details gathered from a monitored field with a way of wireless links. The information is to be forwarded through multiple nodes within the gateway. This data is to be connected to other networks like wireless Ethernet. It consists of base stations and various numbers of nodes. This network is to be used to diagnose physical or environment conditions such as sound, pressure, temperature and co operatively which is pass information through the network to a main location. In the radio communication networks, the wireless sensor network has the various number structures with various topologies. In the wireless sensor network, there are various attacks are to be presented according to the different criteria such as domain of the attackers or the techniques which are to be used in attacks. There are two major categories are to be classified which are according the
interruption of communication like passive attacks and active attacks. For this, software defined network is an efficient one for enhancing security against attack by maintaining QoS. 1.1. Clone Attack The wireless sensor network is most vulnerable which the severe attack that is clone attack is. This method helps to detect the clone attacks which are present in the wireless sensor networks. There are various centralized and distributed techniques such as on the detection of clones in sensor networks using random key pre distribution, detecting node clones in sensor networks, real time detection of clone attacks in wireless sensor network, hierarchical node replication attacks detection in wireless sensor network, compressed sensing based clone identification in the sensor networks, fast detection of replica node
∗ Corresponding author. E-mail addresses: [email protected] (P.P. Devi), [email protected] (B. Jaison).
https://doi.org/10.1016/j.comcom.2020.01.064 Received 25 September 2019; Received in revised form 23 December 2019; Accepted 27 January 2020 Available online xxxx 0140-3664/© 2020 Elsevier B.V. All rights reserved.
P.P. Devi and B. Jaison
Computer Communications 152 (2020) 316–322
keys, social fingerprint verification, distributed detection techniques, node to network broadcasting, randomized multicast and line selected multicast, random walk based approach, randomized efficient and distributed protocol, memory efficient protocols for replica detection, localized multicast protocol, randomized directed exploration and performance analysis. Shriya V.Autkar [3] et al. proposed a survey on distributed techniques for detection of node clones in wireless sensor networks. In the wireless sensor network, the clone attack detection is the major issues which are to preserve the security goals. This paper has following information such as node clone attack, motivation, node clone detection techniques, distributed node clone detection protocols in static wireless sensor network, node to network broadcast, witness based methods, limitations of various existing methods, energy efficient distributed protocol and performance analysis. In this method, the communication and storage components are to be detected the node clones which should be less when the resource constraints are to be applied in the wireless sensor network. Mumtaz Qabulio [4] et al. proposed a securing mobile wireless sensor networks against clone node attack. This method used for identify the physical location information or any other difficult computational intensive algorithm. This technique is most suitable for memory and the computationally constrained sensor nodes. This paper has the following information such as adversary model, various assumptions conditions, and theoretical analytics of the proposed methodology, practical analysis of the proposed scheme, and performance analysis. In this paper, the re-verification module is to be integrated. It is used for traditional cryptographic solutions which are to authenticate the network nodes. The further development of this paper is to be planned which is to compute the computation and communication overheads present in the real world environment. Mohammad Y. Aalsalem [5] et al. proposed detecting clones in wireless sensor networks using constrained random walk. In this paper, the random walk (RAWL) is the witness node which is depends on distributed techniques. It is randomly selected with the help of initiating several random walks throughout the network. The RAWL used to be achieved high security of witness nodes but in accomplishing high prediction probability RAWL suffers from very high communication and memory overhead. This paper has following information such as network and adversary model, CRAWL, probability of clone detection, communication and memory overhead, and performance analysis. This method helps to enhance the reduction of communication and memory overheads and also achieves the same level of security with high detection probability. Zhongming Zheng [6] et al. proposed energy and memory efficient clone detection in wireless sensor networks. This method can guarantee achieve clone attack detection and maintain the satisfactory network lifetime. This method has the ring structure which has the energy efficient data forwarding along the path toward the witness and the sink. The further extension of this paper is to analyze the clone detection performance with the untrustful witness. The simulation and result shows that there is the analysis of the proposed protocol which can be achieved high network lifetime with the help of the effectively distributing the traffic load over the network. This paper has the following information such as system model and problem statement, ERCD protocol, performance analysis, probability of clone detection, energy consumption and network lifetime, data buffer capacity, and performance analysis. Antonio Nappa [7] et al. proposed an attack of the clones with a study of the impact of shared code on vulnerability patching. In this method, the deployment process is to be analyzed based on the 1593 vulnerabilities form most popular client applications. This paper has following information such as security model for patching vulnerabilities, threats shared code and multiple installations, goal and non goals, various data sets, vulnerability analysis, data preprocessing, mapping files to program versions, generating vulnerability reports, survival analysis, threats validity, patching in different applications, patching delay, patches and exploits, opportunities for patch based exploit generation, impact of multiple installations on patch deployment, patching milestones,
Fig. 1. Clone Attack Analysis.
attack in mobile sensor networks using sequential analysis, protocol detection of node replication attacks in mobile wireless sensor networks, randomized, efficient and distributed protocol for the detection of node replication attacks in wireless sensor networks, distributed detection of node capture attacks in wireless sensor networks, randomly directed exploration, random walk based approach to detect clone attacks in wireless sensor networks, and mobility assisted detection of the replication in mobile wireless sensor network. The Fig. 1 shows that there is analysis of clone attack present in wireless sensor network. 1.2. Key Distribution in WSN In wireless sensor networks, the key distribution is the most important problem for designing the networks. The key pre distribution is the technique of keys onto various nodes before deployment. The nodes build up the network with the help their secret keys after deployment. There are various schemes are to be developed with better maintenance of PEA management in WSNs. This method has 3 phases such as key distribution, shared key discovery, and path-key establishment. In these phases, the secret keys are to be generated and it is placed in sensor nodes. The paths are to be established which is connecting these links which is to create a connected graph. In this key distribution, the most critical ones are local and global connectivity and resiliency. The keys are to be generated with random manner which helps to determine mutual connectivity. The Fig. 2 shows that there is analysis of key pool scheme. 2. Literature Review Balmukund Mishra [1] et al. proposed an approach toward the optimization of wireless based node clone attack. This paper helps to analyze the clone detection method present in the distributed node. This method used as efficient protocols such as LSM and RED based on different category. These protocols help to analyze the detection level, energy overhead and memory. This method gives the approach for the optimization technique based on the witness distributed node cone detection. This paper has following information such as network model, threat model, proposed approach, security analysis, and performance analysis. The simulation and result gives the details about the detection level and energy of different algorithms. Rubal Grewal [2] et al. proposed a survey on proficient techniques to mitigate clone attack in wireless sensor network. The clone attack has the compromises a node which is extracting all the credentials such as codes are stored, keys, and identity. In this method, the replica detection is the most challenging task in the field of security. This paper has following information such as centralized detection techniques, straightforward approach, set operations, cluster based approach, detecting replicated 317
P.P. Devi and B. Jaison
Computer Communications 152 (2020) 316–322
Fig. 2. Key Pool Scheme with Key Distribution Process.
human factors affecting the update deployment and performance analysis. Tonghui Guan [8] et al. proposed a node clone attack detection scheme based on digital watermark in wireless sensor networks. This method helps to analyze the reversible watermarking scheme and it is based on data sampling interval against clone attack. This paper has the following information such as watermarking based interval, watermark embedding, watermarking detection, statistics probability, attack detection rate, robustness analysis, overhead analysis, and performance analysis. The further development of this paper is to detect the poor analysis when the frequency of the injection false data is very small. Pinaki Sankar Chatterjee [9] et al. proposed a lightweight cloned node detection algorithm for efficiently handling SSDF attacks and facilitating secure spectrum allocation in CWSNs. When the node cloning attack is to be presented, the SSDF attack gives the solution is more difficult. The false spectrum sensing reports will be sensed in CWSNs with the help of the maximum match filtering. This paper has the following information such as LEACH protocol, motivation and contributions, CUCKOO filter, multiple linear regressions, monitor node election and spectrum sensing, cloned node detection, SSDF attack prevention, attacker detection probability of the MMF algorithm in the absence of node cloning attackers, attackers detection probability of the MMF algorithm in the presence of node cloning attackers, and performance analysis. Rijnard van Tonder [10] et al. proposed a defending against the attack of the micro clones. This paper helps to established toward detection and elimination of micro clones at scale. This paper has the following information such as various approached, micro clone detection, micro clone patches, threats and performance analysis. The simulation and result shows that there is the analysis of capability of the leverages to facilitate and automatic removal of the legacy of the micro clones which continue to persist in the high profile software. There are various patches are to be tested when the constituted a real flaw or the repository inactivity. The paper has the 95% of our patches which is to active GitHub repositories which are to be merged rapidly changed.
Fig. 3. Proposed Flow Diagram.
3. Proposed Methodology The hybrid clone node detection (HCND) mechanism based on SDN helps in the detection of clone node present in the wireless network. SDN based paradigm is much efficient in the application of security attack schemes and thereby to maintain or improve the QoS constraints. This is to perform efficient clone detection in such a way to eliminate cloning attack in proactive fashion. To detect clones locally as well as across geographical region through cost effective identity verification procedure. This method helps to protect the wireless sensor network from the node identity replicas using the superimposed SDIS junction code. The node identity replicas help to choose the credible path for successful transmissions. The superimposed method is to be used for
Fig. 4. Clone Attack Detection Process Diagram.
retrieval of information from node participating on the network. To thwart cluster of attacks hosted from the clones, by removing the hosting clones. The Fig. 3 shows that there is the analysis of proposed methodology. 318
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Computer Communications 152 (2020) 316–322
3.1. Clone Attack Detection
3.3. Hybrid Clone Node Detection Process (HCND)
The node is to be captured then the attacker can be reprogrammed and also create a clone of a captured node. The clones are to be deployed in all the area of the network. The clone node attacks are very harmful to the performance of the sensor networks. In the wireless sensor network, the sensor nodes are to be moved their own after the completion of the deployment. There are two types of the approaches are to be followed such as centralized approach and distributed approach. The node detection is to be replicated present in the centralized approach due to a new node joins the network. The distributed approaches helps to detect the clone nodes which is depends on details about location for a node which are to be stored at witness nodes present in the network. In the mobile sensor network, the nodes are to be moved sequentially present in the network and the duplicate nodes are to be detected with various techniques present in the static network which is not applicable. The algorithm shows that there is the analysis of clone attack detection. The Fig. 4 shows that there is the analysis of the detection of clone attack.
In the hybrid clone node detection method, there are various hybrid tools which helps the combination of different syntactic and semantic method. It is used to detect clones which are present in the sensor network. There are various methods are to be utilized which helps to identify different types of clones with high efficiency and accuracy. It is the process of token based mechanisms with the help of abstract syntax tree with a combination of suffix tree algorithms. There are various mechanisms combination of both structural differences detection mechanisms and semantic techniques on programs to predict clones. The Fig. 5 shows that there is the analysis of the hybrid clone node detection process. The API format is to be processed with the help of the byte generator which gives the output of byte code. The PDG is to operate with the help of normalizer and gives the output of adjacency matrix. This matrix is to be filtered. This method is to be applied with the potential of clones which gives the output of actual clones. The algorithm shows that there is the analysis of the clone attack prevention techniques using hybrid clone node detection process.
3.2. Key Distribution Process of Clone Attack The key distribution process helps to reach a secure random encrypted that is cryptographic secret key which is evaluated between the expediting Alice and the designating the Bob. This encryption process is to be used for secure way of performing data and this encoded message is depends on quantum state optical process. We consider the channel like depolarization which is to be defined as the unit operator that is Dc of the quantum state transformation is denoted as 𝜑 which is to be followed below. Dc (|𝜑) = (1 − 𝑐) |𝜑)(𝜑| +
3 𝑐∑ 𝜎 |𝜑) (𝜑| 𝜎𝑝 ) 3 𝑝=1 𝑝
(1)
where 𝑐 is the depolarizing channel parameter and 𝜎𝑝 is the Pauli metrices (𝑝 = 𝑥, 𝑦, 𝑧). In the absence of the attacker the bit error is related on the depolarizing parameter so the bit error is to be evaluated given below,
(2)
Quant BER = 2c∕3
This bit error rate is to be related to the channel visibility Vt as QuantBER which is evaluated given below, Vt as QuantBER = (1 − Vt)∕2
(3)
The attackers are to be presented between the clone which is to be determined given below, the number of attackers Akp (𝑝 = 1, 2, …, 𝑅𝑁), each attacker Akp clones with a cloning transformation Cp and A is the Alice, Cp (|0) A |0) Ak p ) = |0) Ak p |0) Ak p
(4)
4. Simulation and Result Discussion
and
This proposed work therefore attempts a mechanism that implements a network level route analysis and time-based analysis methods which involves a low cost timely monitoring of the environment to identify and avoid redundant nodes which may be caused due to cloning attack. The hybrid clone node detection (HCND) mechanism based on SDN helps to detect the clone node present in the wireless network. This is to perform efficient SDN based clone detection in such a way to eliminate cloning attack in proactive fashion. To detect clones locally as well as across geographical region through cost effective identity verification procedure. Also, the SDN based mechanism for security against attacks helps in maintaining and enhancing QoS. This method helps to protect the wireless sensor network from the node identity replicas using the superimposed SDIS junction code. The simulation result shows that there is the performance analysis of various
Cp (|1) A |0) Ak p ) = |1) Ak p |1) Ak p Akp will use Cp in the basis out defined as follows, Cp (|0) out_A |0) out_AK) = |0) out_A𝐾 |0) out_Ak p
(5)
C (|1) out_A |0) out_Ak p ) = cos (𝜃𝑝 ) |1) out_A |0) out_Ak p + sin (𝜃𝑝 ) |0) out_A |1) out_Ak p
(6)
Here 𝜃𝑝 (0 ≤ 𝜃𝑝 ≤ 𝜋2 ) is the angle of attack of the attacker Akp . After the cloning attack, Akp keeps the photon which belongs originally to its state space. The Alice and the attacker between the photons are to be transmitted which can be depolarized with the help of applying the model of depolarizing channel. 319
P.P. Devi and B. Jaison
Computer Communications 152 (2020) 316–322
Fig. 5. Hybrid Clone Node Detection Process Analysis.
parameters such as false positive, false negative ratio analysis, precision analysis, recall analysis and detection analysis. Our existing method is graph based technique in hybrid code clone detection and our proposed method is HCND mechanism.
ratio of the positives to the detected clones with percentage.
False negative (%) = [N]∕[A] ∗ 100
(7)
False positive (%) = [P]∕[D] ∗ 100
(8)
4.1. False Positive and False Negative Analysis where N denotes the false negatives in percentage, A denoted as actual
The false negative is defined as the ratio of the false negative to the actual clones with percentage and the false positive is defined as the
clones, P is the false positive and D is the detected clones. 320
P.P. Devi and B. Jaison
Computer Communications 152 (2020) 316–322
Fig. 8. Detection of Clone Analysis. Fig. 6. Precision Analysis.
Fig. 7. Recall Analysis. Fig. 9. Correct Detection of Clone Analysis.
4.2. Precision Recall Analysis detect clones locally as well as across geographical region through cost effective identity verification procedure. To maintain or enhance the QoS constraints using SDN based mechanism. This method helps to protect the wireless sensor network from the node identity replicas using the superimposed SDIS junction code. The node identity replicas help to choose the credible path for successful transmissions. The superimposed method is to be used for retrieval of information from node participating on the network. To thwart cluster of attacks hosted from the clones, by removing the hosting clones. The result gives the information about the better performance compare than various existing mechanisms. The proposed methodology gives the better analysis compare than existing methods.
The precision and recall will be analyzed with the quality of the system which can be estimated through the quality metrics. Precision it is to measure the proportion of the actual clones which are to be specified identified. P=
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑐𝑙𝑜𝑛𝑒𝑠 𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑙𝑦 𝑓 𝑜𝑢𝑛𝑑 𝑇 𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑐𝑙𝑜𝑛𝑒𝑠
(9)
Recall: It is measure the proportion of non clones which are to be accurately predicted correct manner. R=
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑐𝑙𝑜𝑛𝑒𝑠 𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑙𝑦 𝑓 𝑜𝑢𝑛𝑑 𝑇 𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑐𝑙𝑜𝑛𝑒𝑠 𝑖𝑛 𝑠𝑜𝑢𝑟𝑐𝑒 𝑐𝑜𝑑𝑒
(10)
The Table 1 shows that there is the analysis of various performance parameters which are to be estimated and tabulated it. The Fig. 6 shows that there is the analysis of precision with various existing methodologies and Fig. 7 shows that there is the analysis of recall with various existing methodologies. The Table 2 shows that there is the analysis of detection of clones with various existing methods. The Fig. 8 denotes the detection clone analysis and Fig. 9 denotes the correctly detected clone analysis.
Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
5. Conclusion Ethical approval
The hybrid clone node detection (HCND) mechanism based on SDN helps to detect the clone node present in the wireless network. This is to perform efficient clone detection in such a way to eliminate cloning attack in proactive fashion using SDN for the cloud framework. To
This article does not contain any studies with human participants or animals performed by any of the authors. 321
P.P. Devi and B. Jaison
Computer Communications 152 (2020) 316–322 Table 1 Performance Parameters Analysis. Methods
Existing precision analysis [11] (%)
Proposed precision analysis (%)
Existing recall analysis (%) [11]
Proposed recall analysis (%)
Token based method AST based method Clone alone based method HCND method
95 97 94 96
97 99 95.5 97.5
90 96 88 97
91 97 90 99
Table 2 Clone Detection Analysis. Methods
Existing detected clone analysis [11]
Proposed detected clone analysis
Existing correctly detected clone analysis [11]
Proposed correctly detected clone analysis
Token based method AST based method Clone alone based method HCND method
192 249 756
194 251 758
183 242 751
192 249 756
807
810
800
809
References
[6] Zhongming Zheng, Anfeng Liu, Lin X. Cai, Zhigang Chen, Xuemin(Sherman) Shen, Energy and Memory Efficient Clone Detection in Wireless Sensor Networks, IEEE, 2015, 1130–1143. [7] Antonio Nappa, Richard Johnson, Leyla Bilge, Juan Caballero, Tudor Dumitras, The Attack of the Clones: A Study of the Impact of Shared Code on Vulnerability Patching, IEEE, 2015, http://dx.doi.org/10.1109/SP.2015.48. [8] Tonghui Guan, Yonghong Chen, A Node Clone Attack Detection Scheme Based on Digital Watermark in WSNs, IEEE, 2016, ISBN: 978-1-4673-8515-2/16. [9] Pinaki Sankar Chatterjee, Monideepa Roy, Lightweight Cloned-Node Detection Algorithm for Efficiently Handling SSDF Attacks and Facilitating Secure Spectrum Allocation in CWSNs, Vol. 8, IEEE, 2018, pp. 121–128, (3). [10] Rijnard van Tonder, Claire Le Goues, Defending Against the Attack of the Micro-Clones, IEEE, 2016, ISBN: 978-1-5090-1428-6/16. [11] Aritra Ghosh, Young Lee, An Empirical Study of a Hybrid Code Clone Detection Approach on Java Byte Code, IEEE, 2017, http://dx.doi.org/10.5176/2251-3043_ 5.2.367.
[1] Balmukund Mishra, Yashwant Singh, An Approach toward the Optimization of Witness Based Node Clone Attack, IEEE, 2015, ISBN: 978-1-5090-0148-4/15. [2] Rubal Grewal, Jasleen Kaur, Kamaljit Singh Saini, A Survey on Proficient Techniques to Mitigate Clone Attack in Wireless Sensor Networks, IEEE, 2015, ISBN: 978-1-4799-8047-5/15. [3] Shriya V. Autkar, M.R. Dhage, S.P. Bholane, A Survey on Distributed Techniques for Detection of Node Clones in Wireless Sensor Networks, IEEE, 2015, ISBN: 978-1-4799-6272-3/15. [4] Mumtaz Qabulio, Yasir Arfat Malkani, Ayaz Keerio, Securing Mobile Wireless Sensor Networks (WSNs) against Clone Node Attack, IEEE, 2015, ISBN: 978-1-4673-7914-4/15. [5] Mohammad Y. Aalsalem1, Wazir Zada Khan, N.M. Saad, Detecting Clones in Wireless Sensor Networks using Constrained Random Walk, IEEE, 2015, ISBN: 978-1-4673-9424-6/15.
322
Contents lists available at ScienceDirect
Computer Communications journal homepage: www.elsevier.com/locate/comcom
Protection on Wireless Sensor Network from Clone Attack using the SDN-Enabled Hybrid Clone Node Detection Mechanisms P.P. Devi a ,∗, B. Jaison b a b
Aalim Muhammed Salegh College of Engineering, India R.M.K Engineering College, India
ARTICLE
INFO
Keywords: Clone attack Performance analysis Hybrid clone node detection Key distribution Wireless sensor network SDN QoS
ABSTRACT WSN is an infrastructure less network that consists of mobile nodes that communicate with each other over wireless links. WSN is vulnerable to the node replication attack (clone attack). Attackers through compromising one sensor node replicate many clones having the same identity (ID) from the compromised node, and place these clones in various places of network. Clones contain all the credentials of legitimate member so appears authentic. This makes the conventional cryptographic tools useless and clone detection difficult. Once the node replication attack has been successful it can help the attacker to exploit almost all of the network operations, like routing, data collection, and key distribution, and also to help launch various other attacks such as black hole, wormhole etc. This proposed work therefore attempts a SDN based mechanism that implements a network level route analysis and time-based analysis methods which involves a low cost timely monitoring of the environment to identify and avoid redundant nodes which may be caused due to cloning attack. Thus, the SDN based cyber security applications are most useful in this situation. The implementation of this SDN based mechanism in WSN helps in maintaining and improving the QoS (Quality of service) constraints. The hybrid clone node detection (HCND) mechanism helps to detect the clone node present in the wireless network. This is to perform efficient clone detection in such a way to eliminate cloning attack in proactive fashion. To detect clones locally as well as across geographical region through cost effective identity verification procedure. This method helps to protect the wireless sensor network from the node identity replicas using the superimposed SDIS junction code. The node identity replicas help to choose the credible path for successful transmissions. The superimposed method is to be used for retrieval of information from node participating on the network. To thwart cluster of attacks hosted from the clones, by removing the hosting clones. The simulation result shows that there is the performance analysis of various parameters such as false positive, false negative ratio analysis, precision analysis, recall analysis and detection analysis.
1. Introduction The wireless sensor network is defined as a network of devices which is to communicate the details gathered from a monitored field with a way of wireless links. The information is to be forwarded through multiple nodes within the gateway. This data is to be connected to other networks like wireless Ethernet. It consists of base stations and various numbers of nodes. This network is to be used to diagnose physical or environment conditions such as sound, pressure, temperature and co operatively which is pass information through the network to a main location. In the radio communication networks, the wireless sensor network has the various number structures with various topologies. In the wireless sensor network, there are various attacks are to be presented according to the different criteria such as domain of the attackers or the techniques which are to be used in attacks. There are two major categories are to be classified which are according the
interruption of communication like passive attacks and active attacks. For this, software defined network is an efficient one for enhancing security against attack by maintaining QoS. 1.1. Clone Attack The wireless sensor network is most vulnerable which the severe attack that is clone attack is. This method helps to detect the clone attacks which are present in the wireless sensor networks. There are various centralized and distributed techniques such as on the detection of clones in sensor networks using random key pre distribution, detecting node clones in sensor networks, real time detection of clone attacks in wireless sensor network, hierarchical node replication attacks detection in wireless sensor network, compressed sensing based clone identification in the sensor networks, fast detection of replica node
∗ Corresponding author. E-mail addresses: [email protected] (P.P. Devi), [email protected] (B. Jaison).
https://doi.org/10.1016/j.comcom.2020.01.064 Received 25 September 2019; Received in revised form 23 December 2019; Accepted 27 January 2020 Available online xxxx 0140-3664/© 2020 Elsevier B.V. All rights reserved.
P.P. Devi and B. Jaison
Computer Communications 152 (2020) 316–322
keys, social fingerprint verification, distributed detection techniques, node to network broadcasting, randomized multicast and line selected multicast, random walk based approach, randomized efficient and distributed protocol, memory efficient protocols for replica detection, localized multicast protocol, randomized directed exploration and performance analysis. Shriya V.Autkar [3] et al. proposed a survey on distributed techniques for detection of node clones in wireless sensor networks. In the wireless sensor network, the clone attack detection is the major issues which are to preserve the security goals. This paper has following information such as node clone attack, motivation, node clone detection techniques, distributed node clone detection protocols in static wireless sensor network, node to network broadcast, witness based methods, limitations of various existing methods, energy efficient distributed protocol and performance analysis. In this method, the communication and storage components are to be detected the node clones which should be less when the resource constraints are to be applied in the wireless sensor network. Mumtaz Qabulio [4] et al. proposed a securing mobile wireless sensor networks against clone node attack. This method used for identify the physical location information or any other difficult computational intensive algorithm. This technique is most suitable for memory and the computationally constrained sensor nodes. This paper has the following information such as adversary model, various assumptions conditions, and theoretical analytics of the proposed methodology, practical analysis of the proposed scheme, and performance analysis. In this paper, the re-verification module is to be integrated. It is used for traditional cryptographic solutions which are to authenticate the network nodes. The further development of this paper is to be planned which is to compute the computation and communication overheads present in the real world environment. Mohammad Y. Aalsalem [5] et al. proposed detecting clones in wireless sensor networks using constrained random walk. In this paper, the random walk (RAWL) is the witness node which is depends on distributed techniques. It is randomly selected with the help of initiating several random walks throughout the network. The RAWL used to be achieved high security of witness nodes but in accomplishing high prediction probability RAWL suffers from very high communication and memory overhead. This paper has following information such as network and adversary model, CRAWL, probability of clone detection, communication and memory overhead, and performance analysis. This method helps to enhance the reduction of communication and memory overheads and also achieves the same level of security with high detection probability. Zhongming Zheng [6] et al. proposed energy and memory efficient clone detection in wireless sensor networks. This method can guarantee achieve clone attack detection and maintain the satisfactory network lifetime. This method has the ring structure which has the energy efficient data forwarding along the path toward the witness and the sink. The further extension of this paper is to analyze the clone detection performance with the untrustful witness. The simulation and result shows that there is the analysis of the proposed protocol which can be achieved high network lifetime with the help of the effectively distributing the traffic load over the network. This paper has the following information such as system model and problem statement, ERCD protocol, performance analysis, probability of clone detection, energy consumption and network lifetime, data buffer capacity, and performance analysis. Antonio Nappa [7] et al. proposed an attack of the clones with a study of the impact of shared code on vulnerability patching. In this method, the deployment process is to be analyzed based on the 1593 vulnerabilities form most popular client applications. This paper has following information such as security model for patching vulnerabilities, threats shared code and multiple installations, goal and non goals, various data sets, vulnerability analysis, data preprocessing, mapping files to program versions, generating vulnerability reports, survival analysis, threats validity, patching in different applications, patching delay, patches and exploits, opportunities for patch based exploit generation, impact of multiple installations on patch deployment, patching milestones,
Fig. 1. Clone Attack Analysis.
attack in mobile sensor networks using sequential analysis, protocol detection of node replication attacks in mobile wireless sensor networks, randomized, efficient and distributed protocol for the detection of node replication attacks in wireless sensor networks, distributed detection of node capture attacks in wireless sensor networks, randomly directed exploration, random walk based approach to detect clone attacks in wireless sensor networks, and mobility assisted detection of the replication in mobile wireless sensor network. The Fig. 1 shows that there is analysis of clone attack present in wireless sensor network. 1.2. Key Distribution in WSN In wireless sensor networks, the key distribution is the most important problem for designing the networks. The key pre distribution is the technique of keys onto various nodes before deployment. The nodes build up the network with the help their secret keys after deployment. There are various schemes are to be developed with better maintenance of PEA management in WSNs. This method has 3 phases such as key distribution, shared key discovery, and path-key establishment. In these phases, the secret keys are to be generated and it is placed in sensor nodes. The paths are to be established which is connecting these links which is to create a connected graph. In this key distribution, the most critical ones are local and global connectivity and resiliency. The keys are to be generated with random manner which helps to determine mutual connectivity. The Fig. 2 shows that there is analysis of key pool scheme. 2. Literature Review Balmukund Mishra [1] et al. proposed an approach toward the optimization of wireless based node clone attack. This paper helps to analyze the clone detection method present in the distributed node. This method used as efficient protocols such as LSM and RED based on different category. These protocols help to analyze the detection level, energy overhead and memory. This method gives the approach for the optimization technique based on the witness distributed node cone detection. This paper has following information such as network model, threat model, proposed approach, security analysis, and performance analysis. The simulation and result gives the details about the detection level and energy of different algorithms. Rubal Grewal [2] et al. proposed a survey on proficient techniques to mitigate clone attack in wireless sensor network. The clone attack has the compromises a node which is extracting all the credentials such as codes are stored, keys, and identity. In this method, the replica detection is the most challenging task in the field of security. This paper has following information such as centralized detection techniques, straightforward approach, set operations, cluster based approach, detecting replicated 317
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Fig. 2. Key Pool Scheme with Key Distribution Process.
human factors affecting the update deployment and performance analysis. Tonghui Guan [8] et al. proposed a node clone attack detection scheme based on digital watermark in wireless sensor networks. This method helps to analyze the reversible watermarking scheme and it is based on data sampling interval against clone attack. This paper has the following information such as watermarking based interval, watermark embedding, watermarking detection, statistics probability, attack detection rate, robustness analysis, overhead analysis, and performance analysis. The further development of this paper is to detect the poor analysis when the frequency of the injection false data is very small. Pinaki Sankar Chatterjee [9] et al. proposed a lightweight cloned node detection algorithm for efficiently handling SSDF attacks and facilitating secure spectrum allocation in CWSNs. When the node cloning attack is to be presented, the SSDF attack gives the solution is more difficult. The false spectrum sensing reports will be sensed in CWSNs with the help of the maximum match filtering. This paper has the following information such as LEACH protocol, motivation and contributions, CUCKOO filter, multiple linear regressions, monitor node election and spectrum sensing, cloned node detection, SSDF attack prevention, attacker detection probability of the MMF algorithm in the absence of node cloning attackers, attackers detection probability of the MMF algorithm in the presence of node cloning attackers, and performance analysis. Rijnard van Tonder [10] et al. proposed a defending against the attack of the micro clones. This paper helps to established toward detection and elimination of micro clones at scale. This paper has the following information such as various approached, micro clone detection, micro clone patches, threats and performance analysis. The simulation and result shows that there is the analysis of capability of the leverages to facilitate and automatic removal of the legacy of the micro clones which continue to persist in the high profile software. There are various patches are to be tested when the constituted a real flaw or the repository inactivity. The paper has the 95% of our patches which is to active GitHub repositories which are to be merged rapidly changed.
Fig. 3. Proposed Flow Diagram.
3. Proposed Methodology The hybrid clone node detection (HCND) mechanism based on SDN helps in the detection of clone node present in the wireless network. SDN based paradigm is much efficient in the application of security attack schemes and thereby to maintain or improve the QoS constraints. This is to perform efficient clone detection in such a way to eliminate cloning attack in proactive fashion. To detect clones locally as well as across geographical region through cost effective identity verification procedure. This method helps to protect the wireless sensor network from the node identity replicas using the superimposed SDIS junction code. The node identity replicas help to choose the credible path for successful transmissions. The superimposed method is to be used for
Fig. 4. Clone Attack Detection Process Diagram.
retrieval of information from node participating on the network. To thwart cluster of attacks hosted from the clones, by removing the hosting clones. The Fig. 3 shows that there is the analysis of proposed methodology. 318
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3.1. Clone Attack Detection
3.3. Hybrid Clone Node Detection Process (HCND)
The node is to be captured then the attacker can be reprogrammed and also create a clone of a captured node. The clones are to be deployed in all the area of the network. The clone node attacks are very harmful to the performance of the sensor networks. In the wireless sensor network, the sensor nodes are to be moved their own after the completion of the deployment. There are two types of the approaches are to be followed such as centralized approach and distributed approach. The node detection is to be replicated present in the centralized approach due to a new node joins the network. The distributed approaches helps to detect the clone nodes which is depends on details about location for a node which are to be stored at witness nodes present in the network. In the mobile sensor network, the nodes are to be moved sequentially present in the network and the duplicate nodes are to be detected with various techniques present in the static network which is not applicable. The algorithm shows that there is the analysis of clone attack detection. The Fig. 4 shows that there is the analysis of the detection of clone attack.
In the hybrid clone node detection method, there are various hybrid tools which helps the combination of different syntactic and semantic method. It is used to detect clones which are present in the sensor network. There are various methods are to be utilized which helps to identify different types of clones with high efficiency and accuracy. It is the process of token based mechanisms with the help of abstract syntax tree with a combination of suffix tree algorithms. There are various mechanisms combination of both structural differences detection mechanisms and semantic techniques on programs to predict clones. The Fig. 5 shows that there is the analysis of the hybrid clone node detection process. The API format is to be processed with the help of the byte generator which gives the output of byte code. The PDG is to operate with the help of normalizer and gives the output of adjacency matrix. This matrix is to be filtered. This method is to be applied with the potential of clones which gives the output of actual clones. The algorithm shows that there is the analysis of the clone attack prevention techniques using hybrid clone node detection process.
3.2. Key Distribution Process of Clone Attack The key distribution process helps to reach a secure random encrypted that is cryptographic secret key which is evaluated between the expediting Alice and the designating the Bob. This encryption process is to be used for secure way of performing data and this encoded message is depends on quantum state optical process. We consider the channel like depolarization which is to be defined as the unit operator that is Dc of the quantum state transformation is denoted as 𝜑 which is to be followed below. Dc (|𝜑) = (1 − 𝑐) |𝜑)(𝜑| +
3 𝑐∑ 𝜎 |𝜑) (𝜑| 𝜎𝑝 ) 3 𝑝=1 𝑝
(1)
where 𝑐 is the depolarizing channel parameter and 𝜎𝑝 is the Pauli metrices (𝑝 = 𝑥, 𝑦, 𝑧). In the absence of the attacker the bit error is related on the depolarizing parameter so the bit error is to be evaluated given below,
(2)
Quant BER = 2c∕3
This bit error rate is to be related to the channel visibility Vt as QuantBER which is evaluated given below, Vt as QuantBER = (1 − Vt)∕2
(3)
The attackers are to be presented between the clone which is to be determined given below, the number of attackers Akp (𝑝 = 1, 2, …, 𝑅𝑁), each attacker Akp clones with a cloning transformation Cp and A is the Alice, Cp (|0) A |0) Ak p ) = |0) Ak p |0) Ak p
(4)
4. Simulation and Result Discussion
and
This proposed work therefore attempts a mechanism that implements a network level route analysis and time-based analysis methods which involves a low cost timely monitoring of the environment to identify and avoid redundant nodes which may be caused due to cloning attack. The hybrid clone node detection (HCND) mechanism based on SDN helps to detect the clone node present in the wireless network. This is to perform efficient SDN based clone detection in such a way to eliminate cloning attack in proactive fashion. To detect clones locally as well as across geographical region through cost effective identity verification procedure. Also, the SDN based mechanism for security against attacks helps in maintaining and enhancing QoS. This method helps to protect the wireless sensor network from the node identity replicas using the superimposed SDIS junction code. The simulation result shows that there is the performance analysis of various
Cp (|1) A |0) Ak p ) = |1) Ak p |1) Ak p Akp will use Cp in the basis out defined as follows, Cp (|0) out_A |0) out_AK) = |0) out_A𝐾 |0) out_Ak p
(5)
C (|1) out_A |0) out_Ak p ) = cos (𝜃𝑝 ) |1) out_A |0) out_Ak p + sin (𝜃𝑝 ) |0) out_A |1) out_Ak p
(6)
Here 𝜃𝑝 (0 ≤ 𝜃𝑝 ≤ 𝜋2 ) is the angle of attack of the attacker Akp . After the cloning attack, Akp keeps the photon which belongs originally to its state space. The Alice and the attacker between the photons are to be transmitted which can be depolarized with the help of applying the model of depolarizing channel. 319
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Fig. 5. Hybrid Clone Node Detection Process Analysis.
parameters such as false positive, false negative ratio analysis, precision analysis, recall analysis and detection analysis. Our existing method is graph based technique in hybrid code clone detection and our proposed method is HCND mechanism.
ratio of the positives to the detected clones with percentage.
False negative (%) = [N]∕[A] ∗ 100
(7)
False positive (%) = [P]∕[D] ∗ 100
(8)
4.1. False Positive and False Negative Analysis where N denotes the false negatives in percentage, A denoted as actual
The false negative is defined as the ratio of the false negative to the actual clones with percentage and the false positive is defined as the
clones, P is the false positive and D is the detected clones. 320
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Fig. 8. Detection of Clone Analysis. Fig. 6. Precision Analysis.
Fig. 7. Recall Analysis. Fig. 9. Correct Detection of Clone Analysis.
4.2. Precision Recall Analysis detect clones locally as well as across geographical region through cost effective identity verification procedure. To maintain or enhance the QoS constraints using SDN based mechanism. This method helps to protect the wireless sensor network from the node identity replicas using the superimposed SDIS junction code. The node identity replicas help to choose the credible path for successful transmissions. The superimposed method is to be used for retrieval of information from node participating on the network. To thwart cluster of attacks hosted from the clones, by removing the hosting clones. The result gives the information about the better performance compare than various existing mechanisms. The proposed methodology gives the better analysis compare than existing methods.
The precision and recall will be analyzed with the quality of the system which can be estimated through the quality metrics. Precision it is to measure the proportion of the actual clones which are to be specified identified. P=
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑐𝑙𝑜𝑛𝑒𝑠 𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑙𝑦 𝑓 𝑜𝑢𝑛𝑑 𝑇 𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑐𝑙𝑜𝑛𝑒𝑠
(9)
Recall: It is measure the proportion of non clones which are to be accurately predicted correct manner. R=
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑐𝑙𝑜𝑛𝑒𝑠 𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑙𝑦 𝑓 𝑜𝑢𝑛𝑑 𝑇 𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑐𝑙𝑜𝑛𝑒𝑠 𝑖𝑛 𝑠𝑜𝑢𝑟𝑐𝑒 𝑐𝑜𝑑𝑒
(10)
The Table 1 shows that there is the analysis of various performance parameters which are to be estimated and tabulated it. The Fig. 6 shows that there is the analysis of precision with various existing methodologies and Fig. 7 shows that there is the analysis of recall with various existing methodologies. The Table 2 shows that there is the analysis of detection of clones with various existing methods. The Fig. 8 denotes the detection clone analysis and Fig. 9 denotes the correctly detected clone analysis.
Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
5. Conclusion Ethical approval
The hybrid clone node detection (HCND) mechanism based on SDN helps to detect the clone node present in the wireless network. This is to perform efficient clone detection in such a way to eliminate cloning attack in proactive fashion using SDN for the cloud framework. To
This article does not contain any studies with human participants or animals performed by any of the authors. 321
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Existing precision analysis [11] (%)
Proposed precision analysis (%)
Existing recall analysis (%) [11]
Proposed recall analysis (%)
Token based method AST based method Clone alone based method HCND method
95 97 94 96
97 99 95.5 97.5
90 96 88 97
91 97 90 99
Table 2 Clone Detection Analysis. Methods
Existing detected clone analysis [11]
Proposed detected clone analysis
Existing correctly detected clone analysis [11]
Proposed correctly detected clone analysis
Token based method AST based method Clone alone based method HCND method
192 249 756
194 251 758
183 242 751
192 249 756
807
810
800
809
References
[6] Zhongming Zheng, Anfeng Liu, Lin X. Cai, Zhigang Chen, Xuemin(Sherman) Shen, Energy and Memory Efficient Clone Detection in Wireless Sensor Networks, IEEE, 2015, 1130–1143. [7] Antonio Nappa, Richard Johnson, Leyla Bilge, Juan Caballero, Tudor Dumitras, The Attack of the Clones: A Study of the Impact of Shared Code on Vulnerability Patching, IEEE, 2015, http://dx.doi.org/10.1109/SP.2015.48. [8] Tonghui Guan, Yonghong Chen, A Node Clone Attack Detection Scheme Based on Digital Watermark in WSNs, IEEE, 2016, ISBN: 978-1-4673-8515-2/16. [9] Pinaki Sankar Chatterjee, Monideepa Roy, Lightweight Cloned-Node Detection Algorithm for Efficiently Handling SSDF Attacks and Facilitating Secure Spectrum Allocation in CWSNs, Vol. 8, IEEE, 2018, pp. 121–128, (3). [10] Rijnard van Tonder, Claire Le Goues, Defending Against the Attack of the Micro-Clones, IEEE, 2016, ISBN: 978-1-5090-1428-6/16. [11] Aritra Ghosh, Young Lee, An Empirical Study of a Hybrid Code Clone Detection Approach on Java Byte Code, IEEE, 2017, http://dx.doi.org/10.5176/2251-3043_ 5.2.367.
[1] Balmukund Mishra, Yashwant Singh, An Approach toward the Optimization of Witness Based Node Clone Attack, IEEE, 2015, ISBN: 978-1-5090-0148-4/15. [2] Rubal Grewal, Jasleen Kaur, Kamaljit Singh Saini, A Survey on Proficient Techniques to Mitigate Clone Attack in Wireless Sensor Networks, IEEE, 2015, ISBN: 978-1-4799-8047-5/15. [3] Shriya V. Autkar, M.R. Dhage, S.P. Bholane, A Survey on Distributed Techniques for Detection of Node Clones in Wireless Sensor Networks, IEEE, 2015, ISBN: 978-1-4799-6272-3/15. [4] Mumtaz Qabulio, Yasir Arfat Malkani, Ayaz Keerio, Securing Mobile Wireless Sensor Networks (WSNs) against Clone Node Attack, IEEE, 2015, ISBN: 978-1-4673-7914-4/15. [5] Mohammad Y. Aalsalem1, Wazir Zada Khan, N.M. Saad, Detecting Clones in Wireless Sensor Networks using Constrained Random Walk, IEEE, 2015, ISBN: 978-1-4673-9424-6/15.
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