An Enhanced Sybil Guard to Detect Bots in Online Social Networks
DOI:
https://doi.org/10.13052/jcsm2245-1439.1115Keywords:
Sybil Guard, Random Walks (RW), Sybil, Loop Belief Propagation, behavior based detection, Machine LearningAbstract
Sybil accounts are swelling in popular social networking sites such as Twitter, Facebook etc. owing to cheap subscription and easy access to large masses. A malicious person creates multiple fake identities to outreach and outgrow his network. People blindly trust their online connections and fall into trap set up by these fake perpetrators. Sybil nodes exploit OSN’s ready-made connectivity to spread fake news, spamming, influencing polls, recommendations and advertisements, masquerading to get critical information, launching phishing attacks etc. Such accounts are surging in wide scale and so it has become very vital to effectively detect such nodes. In this research a new classifier (combination of Sybil Guard, Twitter engagement rate and Profile statistics analyser) is developed to combat such Sybil nodes. The proposed classifier overcomes the limitations of structure based, machine learning based and behaviour-based classifiers and is proven to be more accurate and robust than the base Sybil guard algorithm.
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References
List of most-downloaded google play applications (Dec 2020). URL – https://en.wikipedia.org/wiki/List_of_most-downloaded_Google_Play_applications.
M. Al-Qurishi, M. Al-Rakhami, A. Alamri, M. Alrubaian, S. M. M. Rahman, M. S. Hossain, Sybil defense techniques in online social networks:A survey, IEEE Access. 5 (2017) 1200–1219. doi:10.1109/ACCESS.2017.2656635.
Sybil attack (Dec 2020). URL – https://en.wikipedia.org/wiki/Sybil_attack
S. Cresci, A decade of social bot detection, Commun. ACM 63(10) (2020) 72–83. doi:10.1145/3409116. URL – https://doi.org/10.1145/3409116
R. Gunturu, Survey of sybil attacks in social networks, CoRR abs/1504.05522 (2015). arXiv:1504.05522. URL – http://arxiv.org/abs/1504.05522
A. Breuer, R. Eilat, U. Weinsberg, Friend or faux: Graph-based early detection of fake accounts on social networks (2020). arXiv:2004.04834.
T. Gao, J. Yang, W. Peng, L. Jiang, Y. Sun, F. Li, A content-based method for sybil detection in online social networks via deep learning, IEEE Access 8 (2020) 38753–38766. doi:10.1109/ACCESS.2020.297 5877.
S. P. Velayudhan, S. Bhanu, Ubcadet: detection of compromised accounts in twitter based on user behavioural profiling, Multimedia Tools and Applications (2020) 1–37.
D. Kumari, K. Singh, M. Manjul, Performance evaluation of sybil attack in cyber physical system, Procedia Computer Science 167 (2020) 1013–1027, international Conference on Computational Intelligence and Data Science. doi: https://doi.org/10.1016/j.procs.2020.03.401. URL – http://www.sciencedirect.com/science/article/pii/S187705092030867X
R. Alharthi, A. Alhothali, K. Moria, Detecting and characterizing Arab spammers campaigns in twitter, Procedia Computer Science 163 (2019) 248–256, 16th Learning and Technology Conference 2019. Artificial Intelligence and Machine Learning: Embedding the Intelligence. doi: https://doi.org/10.1016/j.procs.2019.12.106. URL – http://www.sciencedirect.com/science/article/pii/S1877050919321453
J. Shu, X. Liu, K. Yang, Y. Zhang, X. Jia, R. H. Deng, Sybsub: Privacy-preserving expressive task subscription with sybil detection in crowdsourcing, IEEE Internet of Things Journal 6(2) (2019) 3003–3013. doi:10.1109/JIOT.2018.2877780.
B. Feng, Q. Li, Y. Ji, D. Guo, X. Meng, Stopping the cyberattack in the early stage: Assessing the security risks of social network users, Security and Communication Networks 2019 (2019) 1–14. doi:10.1155/2019/3053418.
D. Yuan, Y. Miao, N. Z. Gong, Z. Yang, Q. Li, D. Song, Q. Wang, X. Liang, Detecting fake accounts in online social networks at the time of registrations, in: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, CCS ’19, Association for Computing Machinery, New York, NY, USA, 2019, pp. 1423–1438. doi:10.1145/3319535.3363198. URL – https://doi.org/10.1145/3319535.3363198
F. Masood, G. Ammad, A. Almogren, A. Abbas, H. A. Khattak, I. Ud Din, M. Guizani, M. Zuair, Spammer detection and fake user identification on social networks, IEEE Access 7 (2019) 68140–68152.
M. Al-Qurishi, M. Alrubaian, S. M. M. Rahman, A. Alamri, M. M. Hassan, A prediction system of sybil attack in social network using deep regression model, Future Generation Computer Systems 87 (2018) 743–753. doi: https://doi.org/10.1016/j.future.2017.08.030. URL – http://www.sciencedirect.com/science/article/pii/S0167739X17300821
X. Zhang, H. Xie, J. C. S. Lui, Sybil detection in social-activity networks: Modeling, algorithms and evaluations, in: 2018 IEEE 26th International Conference on Network Protocols (ICNP), 2018, pp. 44–54. doi:10.1109/ICNP.2018.00015.
P. Muthusamy, T. Sheela, Sybil attack detection based on authentication process using digital security certificate procedure for data transmission in MANET, International Journal of Engineering Technology 7(3.27) (2018) 270. doi:10.14419/ijet.v7i3.27.17891.
D. Wu, S. Si, H. Wang, R. Wang, J. Yan, Social influence aware sybil detection in social networks, in: 2017 IEEE/CIC International Conference on Communications in China (ICCC), 2017, pp. 1–4.
B. Wang, L. Zhang, N. Z. Gong, Sybilscar: Sybil detection in online social networks via local rule-based propagation, in: IEEE INFOCOM 2017 – IEEE Conference on Computer Communications, 2017, pp. 1–9. doi:10.1109/INFOCOM.2017.8057066.
H. Zheng, M. Xue, H. Lu, S. Hao, H. Zhu, X. Liang, K.W. Ross, Smoke screener or straight shooter: Detecting elite sybil attacks in user-review social networks, ArXiv abs/1709.06916 (2017).
S. Cresci, R. Di Pietro, M. Petrocchi, A. Spognardi, M. Tesconi, Dna-inspired online behavioral modeling and its application to spambot detection, IEEE Intelligent Systems 31(5) (2016) 58–64. doi:10.1109/MIS.2016.29.
N. M. Shekokar, K. B. Kansara, Security against sybil attack in social network, in: 2016 International Conference on Information Communication and Embedded Systems (ICICES), 2016, pp. 1–5. doi:10.1109/ ICICES.2016.7518887.
S. J. Samuel, B. Dhivya, An efficient technique to detect and prevent sybil attacks in social network applications, in: 2015 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), 2015, pp. 1–3. doi:10.1109/ICECCT.2015.7226059.
M. Egele, G. Stringhini, C. Kruegel, G. Vigna, Towards detecting compromised accounts on social networks, IEEE Transactions on Dependable and Secure Computing 14(4) (2017) 447–460. doi:10.1109/ TDSC.2015.2479616.
A. Gaur, R. K. Dubey, V. Ricchariya, Article: An anti-image technique for sybil detection in web data, International Journal of Computer Applications 121(24) (2015) 5–8, full text available.
M. Alsaleh, A. Alarifi, A. M. Al-Salman, M. Alfayez, A. Almuhaysin, Tsd: Detecting sybil accounts in twitter, in: 2014 13th International Conference on Machine Learning and Applications, 2014, pp. 463–469. doi:10.1109/ICMLA.2014.81.
X. Zheng, Z. Zeng, Z. Chen, Y. Yu, C. Rong, Detecting spammers on social networks, Neurocomputing 159 (2015) 27–34. doi: https://doi.org/10.1016/j.neucom.2015.02.047. URL – http://www.sciencedirect.com/science/article/pii/S0925231215002106
W. Zang, P. Zhang, X. Wang, J. Shi, L. Guo, Detecting sybil nodes in anonymous communication systems, Procedia Computer Science 17 (2013) 861–869, first International Conference on Information Technology and Quantitative Management. doi: https://doi.org/10.1016/j.procs.2013.05.110. URL – http://www.sciencedirect.com/science/article/pii/S1877050913002433
Q. Cao, X. Yang, Sybilfence: Improving social-graph-based sybil defenses with user negative feedback, ArXiv abs/1304.3819 (2013).
L. Xu, S. Chainan, H. Takizawa, H. Kobayashi, Resisting sybil attack by social network and network clustering, in: 2010 10th IEEE/IPSJ International Symposium on Applications and the Internet, 2010, pp. 15–21. doi:10.1109/SAINT.2010.32.
Jubins. (n.d.). Jubins/MachineLearning-Detecting-Twitter-Bots. Retrieved from https://github.com/jubins/MachineLearning-Detecting-Twitter-Bots
H. Yu, M. Kaminsky, P. B. Gibbons, A. D. Flaxman, Sybilguard: Defending against sybil attacks via social networks, IEEE/ACM Transactions on Networking 16(3) (2008) 576–589. doi:10.1109/TNET.2008. 923723.
Haifeng Yu, Phillip B. Gibbons, Michael Kaminsky, and Feng Xiao. (2010). SybilLimit: a near-optimal social network defense against sybil attacks. IEEE/ACM Trans. Netw. 18(3), 885–898. doi:https://doi.org/10.1109/TNET.2009.2034047.
Qiang Cao, Michael Sirivianos, Xiaowei Yang, and Tiago Pregueiro. (2012). Aiding the detection of fake accounts in large scale social online services. In Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation (NSDI’12). USENIX Association, USA, 15.
Gong, N., Frank, M., and Mittal, P. (2014). Sybil Belief: A Semi-Supervised Learning Approach for Structure-Based Sybil Detection. IEEE Transactions on Information Forensics and Security, 9, 976–987.
B. Wang, L. Zhang and N. Z. Gong.(2017). Sybil SCAR: Sybil detection in online social networks via local rule based propagation, IEEE INFOCOM 2017 – IEEE Conference on Computer Communications, Atlanta, GA, 1–9, doi: 10.1109/INFOCOM.2017.8057066.
George Danezis, Prateek Mittal.(2009). Sybil Infer: Detecting Sybil Nodes using Social Networks. NDSS 2009.
Wei Wei, Fengyuan Xu, C. C. Tan and Qun Li. (2012). SybilDefender: Defend against sybil attacks in large social networks. Proceedings IEEE INFOCOM, Orlando, FL, 1951–1959, doi: 10.1109/INFCOM.2012.6195572.
Wang, G., Mohanlal, M., Wilson, C., Wang, X., Metzger, M.J., Zheng, H., & Zhao, B. (2013). Social Turing Tests: Crowdsourcing Sybil Detection. ArXiv, abs/1205.3856.
J. P. Dickerson, V. Kagan and V. S. Subrahmanian. (2014). Using sentiment to detect bots on Twitter: Are humans more opinionated than bots? IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), Beijing, 620–627, doi: 10.1109/ASONAM.2014.6921650.
Davis, C.A., Varol, O., Ferrara, E., Flammini, A., and Menczer, F. (2016). Bot Or Not: A System to Evaluate Social Bots. Proceedings of the 25th International Conference Companion on World Wide Web.
Wang G, Tristan Konolige, Christo Wilson, Xiao Wang, Haitao Zheng, and Ben Y. Zhao. (2013). You are how you click: clickstream analysis for Sybil detection. In Proceedings of the 22nd USENIX conference on Security. USENIX Association, USA, 241–256.
A. Thobhani, M. Gao, A. Hawbani, S. T. M. Ali, A. Abdussalam, Captcha recognition using deep learning with attached binary images, Electronics 9(9) (2020). doi:10.3390/electronics9091522. URL – https://www.mdpi.com/2079-9292/9/9/1522
Y. Hu, L. Chen, J. Cheng, A captcha recognition technology based on deep learning, in: 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2018, pp. 617–620. doi:10.1109/ICIEA. 2018.8397789.
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