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An attack-Resistant Reputation Management System For Mobile Ad Hoc Networks
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www.elsevier.com/locate/procedia 2018 International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2018 2018 International Conference on Identification, Information and Knowledge 2018 International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2018 attack-Resistant Reputation System For Mobile Ad in the Internet Management of Things, IIKI 2018
An An attack-Resistant Reputation Management System For Mobile Ad Hoc Networks An attack-Resistant Reputation Management System For Mobile Ad a HocWang Networks San-shun Zhanga , Shi-wen , Hui Xiaa,∗, Xiang-guo Chenga Hoc Networks a College of Computer and Technology,aQingdao University, Qingdao 266071, P.R.C San-shun Zhangaa , Science Shi-wen Wanga , Hui Xiaa,∗ a,∗, Xiang-guo Chenga San-shun Zhang , Shi-wen Wang , Hui Xia , Xiang-guo Cheng a a
Abstract
College of Computer Science and Technology, Qingdao University, Qingdao 266071, P.R.C of Computer Science and Technology, Qingdao University, Qingdao 266071, P.R.C
a College
Due to the openness and dynamics of the network topology, the mobile ad hoc network (MANET) is vulnerable to various attacks Abstract by malicious nodes. How to effectively collect trust information and select trusted relay nodes for message delivery is the focus Abstract Due to the research. openness For and this dynamics of the the reputation network topology, mobile to adindicate hoc network (MANET) is vulnerable to various of current reason, value is the proposed the degree of trust of the node. In thisattacks paper, Due to the openness of thecollect network topology, the mobile hoc network (MANET) ismessage vulnerable to various by malicious nodes. and Howdynamics to effectively trust information and select trusted relay nodes for delivery theattacks focus a reputation management system, which composed of the collection ofad reputation information and the calculation ofisreputation by malicious nodes.toFor How towith effectively trust value information andthe select trustedefficiency. relay nodes message the paper, focus of current research. this reason, thecollect reputation proposed tonetwork indicate the degree offor trust of the node.trust Inisthis value, is proposed deal the malicious attacks andisimprove We proposed a delivery new collection of current research. For this reason, the reputation value is proposed to indicate the degree of trust of the node. In this paper, amechanism reputation management system, which composed of the collection of reputation information and the calculation of reputation to optimize the efficiency of trust information collection. A node reputation value calculation model is designed based avalue, management system, composed ofsimulation the collection of demonstrate reputationefficiency. information the of reputation is proposed to deal with thewhich malicious attacks and improve the network We and proposed a model. new trust collection onreputation subjective trust and recommendation trust. Finally, results the effectiveness of calculation this value, is proposed to deal the malicious attacks andcollection. improve the network efficiency. proposedmodel a new collection mechanism to optimize the with efficiency of trust information A node reputation valueWe calculation is trust designed based mechanism to optimize the efficiency of trust information collection. A node reputation value calculation model is designed based on subjective trust and recommendation trust. Finally, simulation results demonstrate the effectiveness of this model. c 2019 The Authors. Published by Elsevier B.V. � on subjective trust and recommendation trust. Finally, simulation results demonstrate the effectiveness of this model. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) c 2019 � 2019 The The Authors. Authors. Published by by Elsevier B.V. committee of the 2018 International Conference on Identification, Information Peer-review under responsibility ofElsevier the scientific © Published B.V. c 2019 � The Authors. Elsevier B.V. This is open access article the license and Knowledge in the Published Internet ofby Things. This is an an open access article under under the CC CC BY-NC-ND BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility ofthe thescientific scientific committee the 2018 International Conference Identification, Information responsibility of committee ofofthe 2018 International Conference on on Identification, Information and mobile ad hoc networks; reputation management system;ofattacks; trustInternational information Conference on Identification, Information Keywords: Peer-review under responsibility the scientific committee the 2018 and Knowledge inInternet the Internet of of Things. Knowledge in the of Things. and Knowledge in the Internet of Things. Keywords: mobile ad hoc networks; reputation management system; attacks; trust information Keywords: mobile ad hoc networks; reputation management system; attacks; trust information
1. Introduction 1. MANET Introduction is a wireless network composed of mobile nodes. It can be dynamically self-organized in any network 1. Introduction topology. In a network, a node can communicate directly with all other nodes in its radio range, while nodes within is a wireless composed of mobilemanner. nodes. It dynamically self-organized any network theMANET communication range network communicate in a multi-hop In can mostbecases, each mobile node with in a limited transMANETInisa anetwork, wirelessa network composed of mobile nodes. It can be nodes dynamically self-organized in nodes any network topology. node can communicate directly with all other in its radio range, while within mission range needs the assistance of its neighbors to complete the data transmission. Therefore, the performance of topology. In a network, a node can communicate directly with all othercases, nodes in itsmobile radio range, while nodes within the communication in a multi-hop manner. In most node reliable with a limited transMANET depends to range a largecommunicate extent on reliable data transmission between nodes. each In order to ensure transmission the communication range communicate a multi-hop manner. In cases, each mobile node with a limited transmission range needs theaassistance of itsinneighbors to complete themost data Therefore, the performance of of data, we need to find trusted transmission node[1]. For this reason, thetransmission. reputation value is proposed to indicate the mission range needs the assistance of its neighbors to complete the data transmission. Therefore, the performance of MANET depends to a large extent on reliable data transmission between nodes. In order to ensure reliable transmission degree of trust of the node. Building a complete reputation model by collecting trust information and calculating trust MANET depends to a large extent on reliable data transmission between nodes. In order to ensure reliable transmission of data,iswe need to find a trusted For this reason, the value is proposed indicate the values currently the main task.transmission The model node[1]. must be able to identify andreputation resist several common typestoof attack[2], of data,of wetrust needoftothe find a trusted transmission node[1]. For this reason, the reputation value is proposed to indicatetrust the degree node. Building a complete reputation model by collecting trust information and calculating such as bad mouthing attack (BMA), collusion attack (COA), conflicting behavior attack (CBA)[3]. degree of trust of the node. Building a complete reputation model by collecting trust information and calculating trust values is currently the main task. The model must be able to identify and resist several common types of attack[2], values currently theattack main task. Thecollusion model must able toconflicting identify and resist several common types of attack[2], such asisbad mouthing (BMA), attackbe(COA), behavior attack (CBA)[3]. such as bad mouthing attack (BMA), collusion attack (COA), conflicting behavior attack (CBA)[3]. ∗ Corresponding author. Tel.: +8613708961868 .
E-mail address: [email protected] Corresponding author. Tel.: +8613708961868 . ∗ Corresponding author. Tel.: +8613708961868 . c 2019 The 1877-0509 � Authors. Published by Elsevier B.V. E-mail address: [email protected] address: [email protected] ThisE-mail is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) cunder Peer-review the scientific committee 1877-0509 � 2019responsibility The Authors.of Published by Elsevier B.V.of the 2018 International Conference on Identification, Information and Knowledge in 1877-0509 © 2019 Thearticle Authors. Published by Elsevier Elsevier B.V. B.V. (https://creativecommons.org/licenses/by-nc-nd/4.0/) cof 2019 1877-0509 � The Authors. Published by the Internet Things. This is an open access under the CC BY-NC-ND license Thisisisan anopen openaccess access article under theBY-NC-ND CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This article under the scientific CC license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the committee of the 2018 International Conference on Identification, Information and Knowledge in Peer-review under responsibility of the scientific committee of the 2018 International Conference on Identification, Information and Peer-review under responsibility of the scientific committee of the 2018 International Conference on Identification, Information and Knowledge in the Internet ofinThings. Knowledge the Internet of Things. the Internet of Things. 10.1016/j.procs.2019.01.275 ∗
San-shun Zhang et al. / Procedia Computer Science 147 (2019) 473–479 S. Zhang, S. Wang et al. / Procedia Computer Science 00 (2019) 000–000
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2. Related Works In the direct trust model[4], a node directly monitors a neighbor node and calculates its trust value; each observation record is recorded in the experience cache, and the old observation uses the attenuation function to assign weights. A trust establishment strategy based on local voting is presented in [5], in which the trust value of the evaluated node converges based on the recommended values of all the neighbor nodes. The subjective security mechanism based on privacy protection by Wang et al. [6] to incentive nodes to cooperate and establish secure and reliable routing.Shabut et al. [7] proposed a recommendation-based trust model with a defense scheme, which utilized the clustering technique to dynamically filter out attacks related to dishonest recommendations between certain times. A trust model based on a linear combination of self-evaluation and recommended values is proposed in [8]. A time-sensitive and contextdependent reputation model is proposed in [9] for MANETs, where the reputation is a combination of direct trust and recommended trust. Xia et al. [10] demonstrated a layered trust management model based on the vehicular ad hoc network. Shen et al. [11] presented a hierarchical account-aided reputation management system to effectively provide cooperation incentives. However, there are some problems with the above mentioned trust mechanisms in mobile ad hoc environments, such as the selection of trust attributes, the calculation of their weights and lack of direct interaction experience. In view of the above problems, we propose a novel reputation management system. The remainder of this paper is organized as follows. Section 3 describes the collection of reputation information; Section 4 explains the reputation value calculation; Section 5 presents the simulation results, and Section 6 concludes this paper. 3. The Collection of Reputation Information The collection of reputation information is the foundation of the establishment of a reputation management system. In order to effectively collect reputation information in the network, we divide all nodes in the network into two categories: common nodes and management nodes. Each normal node has a monitor to monitor the behavior of its neighbors and report the information to the management node, while the management node is responsible for calculating and saving the reputation value of the corresponding node. A consistent hash algorithm[12] is used to complete the mapping of common nodes to management nodes, so that each common node corresponds to a management node. However, the monitoring information of the common node can be collected by multiple adjacent management nodes. After receiving the monitoring information, the management node sends the report to the actual management node of the monitored node. The actual management node filters the monitoring information to eliminate the error message, and then calculates and saves the reputation value of the node. Since any one of the management nodes can send information to other managers, the information transmission between the management nodes can use a multicast routing protocol such as MCLSPM[13]. This protocol can increase the forwarding rate and reduce latency of reputation information. 4. Reputation Calculation Model 4.1. Calculation of subjective trust The reputation value of nodes consists of the subjective trust and the recommendation trust. You can find that the subjective trust appears in all trust models[14]. To simplify the discussion and implementation, we establish a simple inference method. Assume that there are n-th number of interactions between couple of nodes, i.e. node i and node j. And IEk , IPk and IAk , represent the interaction evaluation, the interaction period and the interaction amount of the k-th interaction, respectively. Then we put forward a quality model to synthesize the abovementioned interaction factors. k(1 ≤ k ≤ n), can be calculated by using (1): ⎧ ⎪ S T i0j = T hreshold, i f k = 0 ⎪ ⎪ ⎨ k � ⎪ ⎪ ⎪ Wm IEm , i f 1 ≤ k ≤ n ⎩ S T ikj = m=1
(1)
San-shun Zhang et al. / Procedia Computer Science 147 (2019) 473–479 S. Zhang, S. Wang et al. / Procedia Computer Science 00 (2019) 000–000
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where S T ikj represents the subjective trust for node j, which can be derived from node i after the k-th interaction and Wm represents the weight of IEm . As shown in Equation (2), the final subjective trust is a weighted average value of all the interaction evaluations that occurred during different interaction periods. We set a value interval for the variables, IEm and S T ikj (i.e. 0 ≤ IEk , S T ikj ≤ 1). At the beginning of an experimental simulation, we initially set the subjective trust value to 0.5 (i.e. the threshold value). The two factors (i.e. IP and IA) are involved into calculating the weight. A rational solution is carried out using the following equation: Wm =
ρm,k IAm k �
m=1
(2)
ρm,k IAm
where ρm,k represents the time attenuation function. An effective attenuation approach could be used to accelerate the convergence rate of a computing process and to guarantee that this process reaches a stable state. To effectively k � calculate the subjective trust, the recent interaction should be given a bigger weight, i.e. ρm,k = 1 and ρ1,k < 1≤m≤k,m=1
ρ2,k < ... < ρk,k . To address the above issues, we introduce a simple model that considers the inter-action period through the following equation: ρm,k =
Tm k �
m=1
(3)
Tm
where T m denotes the beginning time of the m-th interaction period. 4.2. Calculation of recommendation trust 4.2.1. Recommendation credibility In this paper, a recommendation credibility calculation method based on feed-back mechanism is proposed in this paper, which is called feedback credibility FCi,k . The real trust value RVi, j can be obtained according to the result of the interaction when the interaction between node i and node j is completed. Then, the accuracy of the recommendation trust provided by recommendation node kbefore the interaction is verified based on the true trust value. If |RVi, j − S T�k, j | < d , the �deviation is small, indicating that the recommendation node k provides a correct recommendation; If ��RVi, j − S T k, j �� ≥ d , the deviation is large, indicating that the recommendation node k provides a bad recommendation. In this paper, the value of feedback credibility is defined as FCi,k ∈ [0, 1] , which is judged by the feedback mechanism, the specific process is as follows: if the correct recommendation is provided, the feedback credibility will gradually increase, but not exceed 1; If the bad recommendation is provided, the feedback credibility will gradually decrease, but not lower than 0; If there is no feedback information, the feedback credibility remains unchanged. The feedback credibility calculation formula is as follows: ⎧ ⎪ FCi,k + r, |RVi, j − S T k, j | < d ⎪ ⎪ ⎨ FCi,k − p, |RVi, j − S T k, j | ≥ d FCi,k = ⎪ (4) ⎪ ⎪ ⎩ FC , NoRecommendation i,k
where r is the reward factor and p is the punishment factor, the feedback credibility decreases faster than the increased speed, that is, p > r . In this paper, the relationship credibility and feedback credibility are both used to obtain the recommended credibility of the node, and the calculation is as follows: Ci,k =
RCi,k + FCi,k 2
where RCi,k has a value range of [0,1], and is calculated separately according to the recommended node type.
(5)
San-shun Zhang et al. / Procedia Computer Science 147 (2019) 473–479 S. Zhang, S. Wang et al. / Procedia Computer Science 00 (2019) 000–000
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4.2.2. Recommendation trust If there is a direct interaction between recommendation node k and evaluation node i, k is called direct recommendation node. The direct trust value of node i to node k is S T i,k , which can be directly used as the relationship credibility of the directly recommended node. The recommendation credibility can be obtained using the formula (5).Therefore, the calculation of direct recommendation trust should consider both the credibility and the number of interactions. The formula is as follows: T Rdi, j
Nd Ci,k Mi,k = × S T k, j N d k=1 Ci,k Mi,k
(6)
k=1
where N d is the number of direct recommendation nodes, and Mi,k is the number of interactions between node i and direct recommendation node k. If there is no direct interaction between the recommendation node k and node i, but there is a common set of interaction nodes S, k is called an indirect recommendation node. In this paper, S imi,k is used to describe the similarity degree for trust evaluation of public interactive nodes with node i and recommendation node k. The cosine similarity function is used to represent the similarity, which is calculated as follows:
S imi,k
S
S T i,s × S T k,s = S S 2 2 S T i,s × S T k,s s=1
s=1
(7)
s=1
where node s ∈ S is the node that has direct interaction with node i and k. In this paper, the similarity S imi,k is used as the relationship credibility of node i to the recommendation node k, and the recommendation credibility is obtained by using formula (5). The indirect recommendation trust is calculated as follows: T Rin i, j
N in Ci,k = × S T k, j in N k=1 Ci,k
(8)
k=1
in
where N is the number of indirect recommendation nodes. Ultimately, the total recommendation trust is obtained by the weighted sum of direct recommendation trust and indirect recommendation trust. The calculation of total recommendation trust as follows: RT i, j =
Nd d N in T Ri, j + k T Rin i, j k N N
(9)
4.3. Calculation of reputation value In a mobile ad hoc network, the calculation of the reputation value considers the subjective and recommendation trust of the node. the metric Ri, j is used to represent the reputation value of a specific node j from the perspective of an evaluating node i. This metric can be calculated via synthesizing the above mentioned trust attributes using the following equation: Ri, j =
1 β ( j) × S T i, j + × RT i, j 1 + β ( j) 1 + β ( j)
where β ( j) is the entity activity of node j. The calculation of β ( j) is as follows: 1 k Φ N + Φ Nj β ( j) = 2
(10)
(11)
1 , δ is a constant greater than 0 and is used to adjust the speed at which the function Φ(x) tends where Φ(x) = 1− x+δ k to zero. N is the number of nodes that participate in the recommendation, and N k is the total number of nodes that have interacted with node j.
San-shun Zhang et al. / Procedia Computer Science 147 (2019) 473–479 S. Zhang, S. Wang et al. / Procedia Computer Science 00 (2019) 000–000
(a)
477 5
(b)
(c)
Fig. 1. Number of reports: (a) five misreporting nodes; (b) 10 misreporting nodes; (c) success of misreporting
5. Simulation Results We simulated this reputation management model using NetLogo[13], a multi-agent visual simulation tool which allows straightforward modeling of parallel and independent agents and simulation of interactions among peers. The simulation mainly focused on the anti-attack capability of the model, including the BMA, COA[14]. 5.1. Performance: false evaluation resilience To test the performance of the model in terms of resisting misreporting, we set some nodes to deliberately slander their neighbors and send a low evaluation randomly chosen from the range [0.3, 0.4] to managers. Figures 1(a) and (b) show the number of honest and false evaluation reports received by a manager throughout the simulation, and (c) shows the success number of misreporting. The experimental results show that the model can effectively resist misreporting attacks. Within a time of 5000 ticks, for the case of five misreporting nodes, the manager received evaluations 387 times, and the number of successes was zero on average. For the case of 10 misreporting nodes, the manager received evaluations 369 times, and the number of successes was 23. Only when misreporting nodes gather around a certain node can improve the success rate. 5.2. Performance: COA resilience We created several colluders in the network to simulate the COA. All colluders move together as a group and misreport their neighbors, and their evaluation value is randomly chosen in the range [0.3, 0.4]. Unlike simple misreporting
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San-shun Zhang et al. / Procedia Computer Science 147 (2019) 473–479 S. Zhang, S. Wang et al. / Procedia Computer Science 00 (2019) 000–000
(a)
(b)
Fig. 2. Number of attacks and their success; (a) three colluders; (b) five colluders
(a)
(b)
Fig. 3. Reputation of node under COA: (a) three colluders; (b) five colluders
attacks, COA can use a quantitative advantage and collusion strategy to achieve success. To test the performance of the model in resisting COAs, we observed the total number of attacks and the number of successful attacks by colluding nodes. Figure 2(a) shows the number of attacks and successes with three colluders; 854 at-tacks were initiated and only 83 were successful. Figure 2(b) shows the number of attacks and successes for five colluders, where there were 459 successes from 1080 attacks. It can be seen that the number of colluders directly determines the probability of success of a COA. However, this success only indicates that false reports are not eliminated, since the existence of integrity evaluation, the reputation will not decline significantly. We therefore observe the reputation of a node under COA. Figure 3 shows the change in reputation of an attacked node; the reputation of the node is affected to varying degrees, and the more colluders, the more obvious the decline in reputation. 6. Conclusion In this paper, we proposed a reputation management model, the managers used to collect information and calculate reputation value, so as to avoid the overflow of trust information and reduce the network load. The efficiency of information exchange between management nodes is improved by multicast way. The simulation results show that the model can effectively identify and eliminate the false information of malicious nodes, at the same time, the mutual supervision between management nodes ensures the credibility of the management node. In this paper, we proposed
San-shun Zhang et al. / Procedia Computer Science 147 (2019) 473–479 S. Zhang, S. Wang et al. / Procedia Computer Science 00 (2019) 000–000
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a reputation management model, the managers used to collect information and calculate reputation value, so as to avoid the overflow of trust information and reduce the network load. The efficiency of information exchange between management nodes is improved by multicast way. The simulation results show that the model can effectively identify and eliminate the false information of malicious nodes, at the same time, the mutual supervision between management nodes ensures the credibility of the management node. Acknowledgements This work is sponsored by the Natural Science Foundation of China (NSFC) under Grant Nos. 61872205 and 61402245, the Project of Shandong Province Higher Educational Science and Technology Program No. J16LN06, the Source Innovation Project of Qingdao No. 18-2-2-56-jch, the Shandong Provincial Natural Science Foundation No. ZR2014FQ010, and the State Foundation of China for Studying Abroad to Visit the United States as a ‘Visiting Scholar’. Corresponding Author’s E-mail: [email protected] References [1] Yingjie Wang, Zhipeng Cai, Xiangrong Tong, Yang Gao, and Guisheng Yin. (2018) “Truthful incentive mechanism with location privacypreserving for mobile crowdsourcing systems.” Computer Network 135: 32-43. [2] Zhipeng Cai, Zaobo He, Xin Guan and Yingshu Li. (2018) “Collective Data-Sanitization for Preventing Sensitive Information Inference Attacks in Social Networks.” IEEE Transactions on Dependable and Secure Computing 15(4): 577-590. [3] Govindan K, Mohapatra P. (2012) “Trust Computations and Trust Dynamics in Mobile Adhoc Networks: A Survey.” IEEE Communications Surveys and Tutorials 14(2): 279-298. [4] Hui Xia, Fu Xiao, San-shun Zhang, Xiang-guo Cheng and Zhen-kuan Pan. (2018) “A Reputation-Based Model for Trust Evaluation in Social Cyber-Physical Systems.” IEEE Transactions on Network Science and Engineering Online, DOI: 10.1109/TNSE.2018.2866783. [5] Jiang T and Baras JS. (2006) “Trust evaluation in anarchy: A case study on autonomous networks.” in 25th IEEE International Conference on Computer Communications, Bar-celona, Catalunya, Spain. [6] Yingjie Wang, Zhipeng Cai, Guisheng Ying, Yang Gao, Xiangrong Tong and Guanying Wu. (2016) “An Incentive Mechanism with Privacy Protection in Mobile Crowdsourcing Systems.” Computer Network. 102: 157-171. [7] Shabut AM, Dahal KP, Bista SK and Awan IU. (2015) “Recommendation Based Trust Model with an Effective Defence Scheme for MANETs.” IEEE Transactions on Mobile Computing, 14(10): 2101-2115 [8] Hui Xia, Zhe-tao Li, Yu-hui Zheng, An-feng Liu, Young-June Choi and Hiroo Sekiya. (2018) “A Novel Light-weight Subjective Trust Inference Framework in MANETs.” IEEE Transactions on Sustainable Computing, Online, DOI: 10.1109/TSUSC.2018.2817219. [9] Liu J, Issarny V. (2004) “Enhanced reputation mechanism for mobile ad hoc networks.” In Jensen C., Poslad S., Dimitrakos T. (eds) Trust Management, iTrust. [10] Hui Xia, San-shun Zhang, Ben-xia Li, Li Li, and Xiang-guo Cheng (2018) “Towards a novel trust-based multicast routing for VANETs.” Security and Communication Networks, https://doi.org/10.1155/2018/7608198 [11] Shen HY, Li Z. (2015) “A Hierarchical Account-Aided Reputation Management System for MANETs.” IEEE-ACM Transactions on network 23(1): 70-84. [12] Zhuojun Duan, Wei Li and Zhipeng Cai. (2017) “Distributed Auctions for Task Assign-ment and Scheduling in Mobile Crowdsensing Systems.” The 37th IEEE International Conference on Distributed Computing Systems, Atlanta, GA, USA. [13] HSIEH YL, WANG KC. (2012) “Dynamic overlay multicast for live multi- media streaming in urban VANETs.” Computer Networks 51(16): 3609-3628. [14] Hui Xia, Ben-xia Li, San-shun Zhang, Shi-wen Wang and Xiang-guo Cheng. (2018) “A Novel Recommendation-based Trust Inference Model for MANETs.” In Chellappan S., Cheng W., Li W. (eds) Wireless Algorithms, Systems, and Applications, Lecture Notes in Computer Science, vol 10874. Springer, Cham. [15] Hui Xia, Jia Yu, Cheng-liang Tian, Zhen-kuan Pan and Edwin Sha. (2016) “Light-weight Trust-enhanced On-demand Multi-path Routing in Mobile Ad Hoc Networks.” Journal of Network and Computer Applications 62: 112-127. [16] U. Wilensky, Netlogo, 1999, http://ccl. northwestern. edu/netlogo/. [17] Zaobo He, Zhipeng Cai, Jiguo Yu, Xiaoming Wang, Yunchuan Sun and Yingshu Li. (2017) “Cost-efficient Strategies for Restraining Rumor Spreading in Mobile Social Net-works.” IEEE Transactions on Vehicular Technology 66(3): 2789-2800.
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Procedia Computer Science 00 (2019) 000–000
ScienceDirect
Procedia Computer Science 00 (2019) 000–000 Procedia Computer Science (2019) 000–000 Procedia Computer Science 14700 (2019) 473–479
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www.elsevier.com/locate/procedia 2018 International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2018 2018 International Conference on Identification, Information and Knowledge 2018 International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2018 attack-Resistant Reputation System For Mobile Ad in the Internet Management of Things, IIKI 2018
An An attack-Resistant Reputation Management System For Mobile Ad Hoc Networks An attack-Resistant Reputation Management System For Mobile Ad a HocWang Networks San-shun Zhanga , Shi-wen , Hui Xiaa,∗, Xiang-guo Chenga Hoc Networks a College of Computer and Technology,aQingdao University, Qingdao 266071, P.R.C San-shun Zhangaa , Science Shi-wen Wanga , Hui Xiaa,∗ a,∗, Xiang-guo Chenga San-shun Zhang , Shi-wen Wang , Hui Xia , Xiang-guo Cheng a a
Abstract
College of Computer Science and Technology, Qingdao University, Qingdao 266071, P.R.C of Computer Science and Technology, Qingdao University, Qingdao 266071, P.R.C
a College
Due to the openness and dynamics of the network topology, the mobile ad hoc network (MANET) is vulnerable to various attacks Abstract by malicious nodes. How to effectively collect trust information and select trusted relay nodes for message delivery is the focus Abstract Due to the research. openness For and this dynamics of the the reputation network topology, mobile to adindicate hoc network (MANET) is vulnerable to various of current reason, value is the proposed the degree of trust of the node. In thisattacks paper, Due to the openness of thecollect network topology, the mobile hoc network (MANET) ismessage vulnerable to various by malicious nodes. and Howdynamics to effectively trust information and select trusted relay nodes for delivery theattacks focus a reputation management system, which composed of the collection ofad reputation information and the calculation ofisreputation by malicious nodes.toFor How towith effectively trust value information andthe select trustedefficiency. relay nodes message the paper, focus of current research. this reason, thecollect reputation proposed tonetwork indicate the degree offor trust of the node.trust Inisthis value, is proposed deal the malicious attacks andisimprove We proposed a delivery new collection of current research. For this reason, the reputation value is proposed to indicate the degree of trust of the node. In this paper, amechanism reputation management system, which composed of the collection of reputation information and the calculation of reputation to optimize the efficiency of trust information collection. A node reputation value calculation model is designed based avalue, management system, composed ofsimulation the collection of demonstrate reputationefficiency. information the of reputation is proposed to deal with thewhich malicious attacks and improve the network We and proposed a model. new trust collection onreputation subjective trust and recommendation trust. Finally, results the effectiveness of calculation this value, is proposed to deal the malicious attacks andcollection. improve the network efficiency. proposedmodel a new collection mechanism to optimize the with efficiency of trust information A node reputation valueWe calculation is trust designed based mechanism to optimize the efficiency of trust information collection. A node reputation value calculation model is designed based on subjective trust and recommendation trust. Finally, simulation results demonstrate the effectiveness of this model. c 2019 The Authors. Published by Elsevier B.V. � on subjective trust and recommendation trust. Finally, simulation results demonstrate the effectiveness of this model. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) c 2019 � 2019 The The Authors. Authors. Published by by Elsevier B.V. committee of the 2018 International Conference on Identification, Information Peer-review under responsibility ofElsevier the scientific © Published B.V. c 2019 � The Authors. Elsevier B.V. This is open access article the license and Knowledge in the Published Internet ofby Things. This is an an open access article under under the CC CC BY-NC-ND BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility ofthe thescientific scientific committee the 2018 International Conference Identification, Information responsibility of committee ofofthe 2018 International Conference on on Identification, Information and mobile ad hoc networks; reputation management system;ofattacks; trustInternational information Conference on Identification, Information Keywords: Peer-review under responsibility the scientific committee the 2018 and Knowledge inInternet the Internet of of Things. Knowledge in the of Things. and Knowledge in the Internet of Things. Keywords: mobile ad hoc networks; reputation management system; attacks; trust information Keywords: mobile ad hoc networks; reputation management system; attacks; trust information
1. Introduction 1. MANET Introduction is a wireless network composed of mobile nodes. It can be dynamically self-organized in any network 1. Introduction topology. In a network, a node can communicate directly with all other nodes in its radio range, while nodes within is a wireless composed of mobilemanner. nodes. It dynamically self-organized any network theMANET communication range network communicate in a multi-hop In can mostbecases, each mobile node with in a limited transMANETInisa anetwork, wirelessa network composed of mobile nodes. It can be nodes dynamically self-organized in nodes any network topology. node can communicate directly with all other in its radio range, while within mission range needs the assistance of its neighbors to complete the data transmission. Therefore, the performance of topology. In a network, a node can communicate directly with all othercases, nodes in itsmobile radio range, while nodes within the communication in a multi-hop manner. In most node reliable with a limited transMANET depends to range a largecommunicate extent on reliable data transmission between nodes. each In order to ensure transmission the communication range communicate a multi-hop manner. In cases, each mobile node with a limited transmission range needs theaassistance of itsinneighbors to complete themost data Therefore, the performance of of data, we need to find trusted transmission node[1]. For this reason, thetransmission. reputation value is proposed to indicate the mission range needs the assistance of its neighbors to complete the data transmission. Therefore, the performance of MANET depends to a large extent on reliable data transmission between nodes. In order to ensure reliable transmission degree of trust of the node. Building a complete reputation model by collecting trust information and calculating trust MANET depends to a large extent on reliable data transmission between nodes. In order to ensure reliable transmission of data,iswe need to find a trusted For this reason, the value is proposed indicate the values currently the main task.transmission The model node[1]. must be able to identify andreputation resist several common typestoof attack[2], of data,of wetrust needoftothe find a trusted transmission node[1]. For this reason, the reputation value is proposed to indicatetrust the degree node. Building a complete reputation model by collecting trust information and calculating such as bad mouthing attack (BMA), collusion attack (COA), conflicting behavior attack (CBA)[3]. degree of trust of the node. Building a complete reputation model by collecting trust information and calculating trust values is currently the main task. The model must be able to identify and resist several common types of attack[2], values currently theattack main task. Thecollusion model must able toconflicting identify and resist several common types of attack[2], such asisbad mouthing (BMA), attackbe(COA), behavior attack (CBA)[3]. such as bad mouthing attack (BMA), collusion attack (COA), conflicting behavior attack (CBA)[3]. ∗ Corresponding author. Tel.: +8613708961868 .
E-mail address: [email protected] Corresponding author. Tel.: +8613708961868 . ∗ Corresponding author. Tel.: +8613708961868 . c 2019 The 1877-0509 � Authors. Published by Elsevier B.V. E-mail address: [email protected] address: [email protected] ThisE-mail is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) cunder Peer-review the scientific committee 1877-0509 � 2019responsibility The Authors.of Published by Elsevier B.V.of the 2018 International Conference on Identification, Information and Knowledge in 1877-0509 © 2019 Thearticle Authors. Published by Elsevier Elsevier B.V. B.V. (https://creativecommons.org/licenses/by-nc-nd/4.0/) cof 2019 1877-0509 � The Authors. Published by the Internet Things. This is an open access under the CC BY-NC-ND license Thisisisan anopen openaccess access article under theBY-NC-ND CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This article under the scientific CC license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the committee of the 2018 International Conference on Identification, Information and Knowledge in Peer-review under responsibility of the scientific committee of the 2018 International Conference on Identification, Information and Peer-review under responsibility of the scientific committee of the 2018 International Conference on Identification, Information and Knowledge in the Internet ofinThings. Knowledge the Internet of Things. the Internet of Things. 10.1016/j.procs.2019.01.275 ∗
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2. Related Works In the direct trust model[4], a node directly monitors a neighbor node and calculates its trust value; each observation record is recorded in the experience cache, and the old observation uses the attenuation function to assign weights. A trust establishment strategy based on local voting is presented in [5], in which the trust value of the evaluated node converges based on the recommended values of all the neighbor nodes. The subjective security mechanism based on privacy protection by Wang et al. [6] to incentive nodes to cooperate and establish secure and reliable routing.Shabut et al. [7] proposed a recommendation-based trust model with a defense scheme, which utilized the clustering technique to dynamically filter out attacks related to dishonest recommendations between certain times. A trust model based on a linear combination of self-evaluation and recommended values is proposed in [8]. A time-sensitive and contextdependent reputation model is proposed in [9] for MANETs, where the reputation is a combination of direct trust and recommended trust. Xia et al. [10] demonstrated a layered trust management model based on the vehicular ad hoc network. Shen et al. [11] presented a hierarchical account-aided reputation management system to effectively provide cooperation incentives. However, there are some problems with the above mentioned trust mechanisms in mobile ad hoc environments, such as the selection of trust attributes, the calculation of their weights and lack of direct interaction experience. In view of the above problems, we propose a novel reputation management system. The remainder of this paper is organized as follows. Section 3 describes the collection of reputation information; Section 4 explains the reputation value calculation; Section 5 presents the simulation results, and Section 6 concludes this paper. 3. The Collection of Reputation Information The collection of reputation information is the foundation of the establishment of a reputation management system. In order to effectively collect reputation information in the network, we divide all nodes in the network into two categories: common nodes and management nodes. Each normal node has a monitor to monitor the behavior of its neighbors and report the information to the management node, while the management node is responsible for calculating and saving the reputation value of the corresponding node. A consistent hash algorithm[12] is used to complete the mapping of common nodes to management nodes, so that each common node corresponds to a management node. However, the monitoring information of the common node can be collected by multiple adjacent management nodes. After receiving the monitoring information, the management node sends the report to the actual management node of the monitored node. The actual management node filters the monitoring information to eliminate the error message, and then calculates and saves the reputation value of the node. Since any one of the management nodes can send information to other managers, the information transmission between the management nodes can use a multicast routing protocol such as MCLSPM[13]. This protocol can increase the forwarding rate and reduce latency of reputation information. 4. Reputation Calculation Model 4.1. Calculation of subjective trust The reputation value of nodes consists of the subjective trust and the recommendation trust. You can find that the subjective trust appears in all trust models[14]. To simplify the discussion and implementation, we establish a simple inference method. Assume that there are n-th number of interactions between couple of nodes, i.e. node i and node j. And IEk , IPk and IAk , represent the interaction evaluation, the interaction period and the interaction amount of the k-th interaction, respectively. Then we put forward a quality model to synthesize the abovementioned interaction factors. k(1 ≤ k ≤ n), can be calculated by using (1): ⎧ ⎪ S T i0j = T hreshold, i f k = 0 ⎪ ⎪ ⎨ k � ⎪ ⎪ ⎪ Wm IEm , i f 1 ≤ k ≤ n ⎩ S T ikj = m=1
(1)
San-shun Zhang et al. / Procedia Computer Science 147 (2019) 473–479 S. Zhang, S. Wang et al. / Procedia Computer Science 00 (2019) 000–000
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where S T ikj represents the subjective trust for node j, which can be derived from node i after the k-th interaction and Wm represents the weight of IEm . As shown in Equation (2), the final subjective trust is a weighted average value of all the interaction evaluations that occurred during different interaction periods. We set a value interval for the variables, IEm and S T ikj (i.e. 0 ≤ IEk , S T ikj ≤ 1). At the beginning of an experimental simulation, we initially set the subjective trust value to 0.5 (i.e. the threshold value). The two factors (i.e. IP and IA) are involved into calculating the weight. A rational solution is carried out using the following equation: Wm =
ρm,k IAm k �
m=1
(2)
ρm,k IAm
where ρm,k represents the time attenuation function. An effective attenuation approach could be used to accelerate the convergence rate of a computing process and to guarantee that this process reaches a stable state. To effectively k � calculate the subjective trust, the recent interaction should be given a bigger weight, i.e. ρm,k = 1 and ρ1,k < 1≤m≤k,m=1
ρ2,k < ... < ρk,k . To address the above issues, we introduce a simple model that considers the inter-action period through the following equation: ρm,k =
Tm k �
m=1
(3)
Tm
where T m denotes the beginning time of the m-th interaction period. 4.2. Calculation of recommendation trust 4.2.1. Recommendation credibility In this paper, a recommendation credibility calculation method based on feed-back mechanism is proposed in this paper, which is called feedback credibility FCi,k . The real trust value RVi, j can be obtained according to the result of the interaction when the interaction between node i and node j is completed. Then, the accuracy of the recommendation trust provided by recommendation node kbefore the interaction is verified based on the true trust value. If |RVi, j − S T�k, j | < d , the �deviation is small, indicating that the recommendation node k provides a correct recommendation; If ��RVi, j − S T k, j �� ≥ d , the deviation is large, indicating that the recommendation node k provides a bad recommendation. In this paper, the value of feedback credibility is defined as FCi,k ∈ [0, 1] , which is judged by the feedback mechanism, the specific process is as follows: if the correct recommendation is provided, the feedback credibility will gradually increase, but not exceed 1; If the bad recommendation is provided, the feedback credibility will gradually decrease, but not lower than 0; If there is no feedback information, the feedback credibility remains unchanged. The feedback credibility calculation formula is as follows: ⎧ ⎪ FCi,k + r, |RVi, j − S T k, j | < d ⎪ ⎪ ⎨ FCi,k − p, |RVi, j − S T k, j | ≥ d FCi,k = ⎪ (4) ⎪ ⎪ ⎩ FC , NoRecommendation i,k
where r is the reward factor and p is the punishment factor, the feedback credibility decreases faster than the increased speed, that is, p > r . In this paper, the relationship credibility and feedback credibility are both used to obtain the recommended credibility of the node, and the calculation is as follows: Ci,k =
RCi,k + FCi,k 2
where RCi,k has a value range of [0,1], and is calculated separately according to the recommended node type.
(5)
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4.2.2. Recommendation trust If there is a direct interaction between recommendation node k and evaluation node i, k is called direct recommendation node. The direct trust value of node i to node k is S T i,k , which can be directly used as the relationship credibility of the directly recommended node. The recommendation credibility can be obtained using the formula (5).Therefore, the calculation of direct recommendation trust should consider both the credibility and the number of interactions. The formula is as follows: T Rdi, j
Nd Ci,k Mi,k = × S T k, j N d k=1 Ci,k Mi,k
(6)
k=1
where N d is the number of direct recommendation nodes, and Mi,k is the number of interactions between node i and direct recommendation node k. If there is no direct interaction between the recommendation node k and node i, but there is a common set of interaction nodes S, k is called an indirect recommendation node. In this paper, S imi,k is used to describe the similarity degree for trust evaluation of public interactive nodes with node i and recommendation node k. The cosine similarity function is used to represent the similarity, which is calculated as follows:
S imi,k
S
S T i,s × S T k,s = S S 2 2 S T i,s × S T k,s s=1
s=1
(7)
s=1
where node s ∈ S is the node that has direct interaction with node i and k. In this paper, the similarity S imi,k is used as the relationship credibility of node i to the recommendation node k, and the recommendation credibility is obtained by using formula (5). The indirect recommendation trust is calculated as follows: T Rin i, j
N in Ci,k = × S T k, j in N k=1 Ci,k
(8)
k=1
in
where N is the number of indirect recommendation nodes. Ultimately, the total recommendation trust is obtained by the weighted sum of direct recommendation trust and indirect recommendation trust. The calculation of total recommendation trust as follows: RT i, j =
Nd d N in T Ri, j + k T Rin i, j k N N
(9)
4.3. Calculation of reputation value In a mobile ad hoc network, the calculation of the reputation value considers the subjective and recommendation trust of the node. the metric Ri, j is used to represent the reputation value of a specific node j from the perspective of an evaluating node i. This metric can be calculated via synthesizing the above mentioned trust attributes using the following equation: Ri, j =
1 β ( j) × S T i, j + × RT i, j 1 + β ( j) 1 + β ( j)
where β ( j) is the entity activity of node j. The calculation of β ( j) is as follows: 1 k Φ N + Φ Nj β ( j) = 2
(10)
(11)
1 , δ is a constant greater than 0 and is used to adjust the speed at which the function Φ(x) tends where Φ(x) = 1− x+δ k to zero. N is the number of nodes that participate in the recommendation, and N k is the total number of nodes that have interacted with node j.
San-shun Zhang et al. / Procedia Computer Science 147 (2019) 473–479 S. Zhang, S. Wang et al. / Procedia Computer Science 00 (2019) 000–000
(a)
477 5
(b)
(c)
Fig. 1. Number of reports: (a) five misreporting nodes; (b) 10 misreporting nodes; (c) success of misreporting
5. Simulation Results We simulated this reputation management model using NetLogo[13], a multi-agent visual simulation tool which allows straightforward modeling of parallel and independent agents and simulation of interactions among peers. The simulation mainly focused on the anti-attack capability of the model, including the BMA, COA[14]. 5.1. Performance: false evaluation resilience To test the performance of the model in terms of resisting misreporting, we set some nodes to deliberately slander their neighbors and send a low evaluation randomly chosen from the range [0.3, 0.4] to managers. Figures 1(a) and (b) show the number of honest and false evaluation reports received by a manager throughout the simulation, and (c) shows the success number of misreporting. The experimental results show that the model can effectively resist misreporting attacks. Within a time of 5000 ticks, for the case of five misreporting nodes, the manager received evaluations 387 times, and the number of successes was zero on average. For the case of 10 misreporting nodes, the manager received evaluations 369 times, and the number of successes was 23. Only when misreporting nodes gather around a certain node can improve the success rate. 5.2. Performance: COA resilience We created several colluders in the network to simulate the COA. All colluders move together as a group and misreport their neighbors, and their evaluation value is randomly chosen in the range [0.3, 0.4]. Unlike simple misreporting
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(a)
(b)
Fig. 2. Number of attacks and their success; (a) three colluders; (b) five colluders
(a)
(b)
Fig. 3. Reputation of node under COA: (a) three colluders; (b) five colluders
attacks, COA can use a quantitative advantage and collusion strategy to achieve success. To test the performance of the model in resisting COAs, we observed the total number of attacks and the number of successful attacks by colluding nodes. Figure 2(a) shows the number of attacks and successes with three colluders; 854 at-tacks were initiated and only 83 were successful. Figure 2(b) shows the number of attacks and successes for five colluders, where there were 459 successes from 1080 attacks. It can be seen that the number of colluders directly determines the probability of success of a COA. However, this success only indicates that false reports are not eliminated, since the existence of integrity evaluation, the reputation will not decline significantly. We therefore observe the reputation of a node under COA. Figure 3 shows the change in reputation of an attacked node; the reputation of the node is affected to varying degrees, and the more colluders, the more obvious the decline in reputation. 6. Conclusion In this paper, we proposed a reputation management model, the managers used to collect information and calculate reputation value, so as to avoid the overflow of trust information and reduce the network load. The efficiency of information exchange between management nodes is improved by multicast way. The simulation results show that the model can effectively identify and eliminate the false information of malicious nodes, at the same time, the mutual supervision between management nodes ensures the credibility of the management node. In this paper, we proposed
San-shun Zhang et al. / Procedia Computer Science 147 (2019) 473–479 S. Zhang, S. Wang et al. / Procedia Computer Science 00 (2019) 000–000
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a reputation management model, the managers used to collect information and calculate reputation value, so as to avoid the overflow of trust information and reduce the network load. The efficiency of information exchange between management nodes is improved by multicast way. The simulation results show that the model can effectively identify and eliminate the false information of malicious nodes, at the same time, the mutual supervision between management nodes ensures the credibility of the management node. Acknowledgements This work is sponsored by the Natural Science Foundation of China (NSFC) under Grant Nos. 61872205 and 61402245, the Project of Shandong Province Higher Educational Science and Technology Program No. J16LN06, the Source Innovation Project of Qingdao No. 18-2-2-56-jch, the Shandong Provincial Natural Science Foundation No. ZR2014FQ010, and the State Foundation of China for Studying Abroad to Visit the United States as a ‘Visiting Scholar’. Corresponding Author’s E-mail: [email protected] References [1] Yingjie Wang, Zhipeng Cai, Xiangrong Tong, Yang Gao, and Guisheng Yin. (2018) “Truthful incentive mechanism with location privacypreserving for mobile crowdsourcing systems.” Computer Network 135: 32-43. [2] Zhipeng Cai, Zaobo He, Xin Guan and Yingshu Li. (2018) “Collective Data-Sanitization for Preventing Sensitive Information Inference Attacks in Social Networks.” IEEE Transactions on Dependable and Secure Computing 15(4): 577-590. [3] Govindan K, Mohapatra P. (2012) “Trust Computations and Trust Dynamics in Mobile Adhoc Networks: A Survey.” IEEE Communications Surveys and Tutorials 14(2): 279-298. [4] Hui Xia, Fu Xiao, San-shun Zhang, Xiang-guo Cheng and Zhen-kuan Pan. (2018) “A Reputation-Based Model for Trust Evaluation in Social Cyber-Physical Systems.” IEEE Transactions on Network Science and Engineering Online, DOI: 10.1109/TNSE.2018.2866783. [5] Jiang T and Baras JS. (2006) “Trust evaluation in anarchy: A case study on autonomous networks.” in 25th IEEE International Conference on Computer Communications, Bar-celona, Catalunya, Spain. [6] Yingjie Wang, Zhipeng Cai, Guisheng Ying, Yang Gao, Xiangrong Tong and Guanying Wu. (2016) “An Incentive Mechanism with Privacy Protection in Mobile Crowdsourcing Systems.” Computer Network. 102: 157-171. [7] Shabut AM, Dahal KP, Bista SK and Awan IU. (2015) “Recommendation Based Trust Model with an Effective Defence Scheme for MANETs.” IEEE Transactions on Mobile Computing, 14(10): 2101-2115 [8] Hui Xia, Zhe-tao Li, Yu-hui Zheng, An-feng Liu, Young-June Choi and Hiroo Sekiya. (2018) “A Novel Light-weight Subjective Trust Inference Framework in MANETs.” IEEE Transactions on Sustainable Computing, Online, DOI: 10.1109/TSUSC.2018.2817219. [9] Liu J, Issarny V. (2004) “Enhanced reputation mechanism for mobile ad hoc networks.” In Jensen C., Poslad S., Dimitrakos T. (eds) Trust Management, iTrust. [10] Hui Xia, San-shun Zhang, Ben-xia Li, Li Li, and Xiang-guo Cheng (2018) “Towards a novel trust-based multicast routing for VANETs.” Security and Communication Networks, https://doi.org/10.1155/2018/7608198 [11] Shen HY, Li Z. (2015) “A Hierarchical Account-Aided Reputation Management System for MANETs.” IEEE-ACM Transactions on network 23(1): 70-84. [12] Zhuojun Duan, Wei Li and Zhipeng Cai. (2017) “Distributed Auctions for Task Assign-ment and Scheduling in Mobile Crowdsensing Systems.” The 37th IEEE International Conference on Distributed Computing Systems, Atlanta, GA, USA. [13] HSIEH YL, WANG KC. (2012) “Dynamic overlay multicast for live multi- media streaming in urban VANETs.” Computer Networks 51(16): 3609-3628. [14] Hui Xia, Ben-xia Li, San-shun Zhang, Shi-wen Wang and Xiang-guo Cheng. (2018) “A Novel Recommendation-based Trust Inference Model for MANETs.” In Chellappan S., Cheng W., Li W. (eds) Wireless Algorithms, Systems, and Applications, Lecture Notes in Computer Science, vol 10874. Springer, Cham. [15] Hui Xia, Jia Yu, Cheng-liang Tian, Zhen-kuan Pan and Edwin Sha. (2016) “Light-weight Trust-enhanced On-demand Multi-path Routing in Mobile Ad Hoc Networks.” Journal of Network and Computer Applications 62: 112-127. [16] U. Wilensky, Netlogo, 1999, http://ccl. northwestern. edu/netlogo/. [17] Zaobo He, Zhipeng Cai, Jiguo Yu, Xiaoming Wang, Yunchuan Sun and Yingshu Li. (2017) “Cost-efficient Strategies for Restraining Rumor Spreading in Mobile Social Net-works.” IEEE Transactions on Vehicular Technology 66(3): 2789-2800.