Method of Detecting and Blocking an Attacker in a Group of Mobile Robots
This article discusses the problem of increasing the resistance to attack by an attacker of a mobile robot groups. The study is limited to the Sibyl attacks and distributed denial of service. An attacker affects the network by sending a large number of packets, or by redirecting the impact on himself. An attacker influences the availability of a group of mobile robots by exerting this impact. This is reflected in the fact that the unit’s battery becomes exhausted quickly. Nodes constantly remain in the active mode, receive packets of an attacker and respond to them. This affects the power consumption of the mobile robot. And also, the attacker also affects the network bandwidth. An attacker blocks useful network traffic by sending a large number of packets to the network and flooding it with requests. Due to congestion of wireless communication channels, collisions and queues of packets may occur, and useful packets with important information can be dropped by mobile robots. The developed method for detecting and blocking malicious nodes is based on an analysis of the parameters of the residual energy and the number of sent/received/redirected/dropped network packets. It is possible to identify a node that demonstrates abnormal behavior by analyzing the degree of deviation of these indicators of each mobile robot relative to the indices of the robot group. Probabilistic methods and methods of mathematical statistics are used for the proposed analysis.
KeywordsMobile robots Protection Vulnerability Anomalies Attacks Credibility Trust Group management Security
This research was carried out in the grant of the Ministry of Education and Science of the Russian Federation Initiative scientific projects No. 2.6244.2017/8.9 on the topic “Developing a method for detecting attacks and intrusions, a method for authenticating nodes for a scalable wireless sensor network”.
- 1.Basan, A.S., Basan, E.S.: A model of threats for the systems of group management of mobile robots. System synthesis and applied synergetic. In: Proceedings of the VIII All-Russian Scientific Conference, pp. 205–212. Southern Federal University, Rostov-on-DonTownship, Lower Arkhyz (2017)Google Scholar
- 3.Vuong, T.P., Loukas, G., Gan, D., Bezemskij, A.: Decision tree-based detection of denial of service and command injection attacks on robotic vehicles. In: Proceedings of 2015 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1–6. IEEE, Rome (2015)Google Scholar
- 4.Bezemskij, A., Loukas, G., Anthony, R.J., Gan. D.: Behaviour-based anomaly detection of cyber-physical attacks on a robotic vehicle. In: Proceedings of 15th International Conference on Ubiquitous Computing and Communications and 2016 8th International Symposium on Cyberspace and Security, pp. 61–68. IEEE, Xi’an (2016)Google Scholar
- 5.Monshizadeh, M., Khatri, V., Kantola, R., Yan, Z.: An Orchestrated security platform for internet of robots. In: Au, M.H.A., Castiglione, A., Choo, K.-K.R., Palmieri, F., Li, K.-C. (eds.) Proceedings of Springer International Conference on Green, Pervasive, and Cloud Computing, vol. 10232, pp. 298– 312. Springer, Cetara (2017)CrossRefGoogle Scholar
- 6.Basan, A.S., Basan, E.S., Makarevich, O.B.: The method of counteracting the attacker’s active attacks in wireless sensor networks. Izvestia of the Southern Federal University. Tech. Sci. 5(190), 16–25 (2017)Google Scholar
- 7.Basan, A.S., Basan, E.S., Makarevich, O.B.: Development of the hierarchal trust management system for mobile cluster–based wireless sensor network. In: Proceeding SIN 2016, Proceedings of the 9th International Conference on Security of Information and Networks, pp. 116–122. Rutgers University, New Jersey (2016)Google Scholar
- 8.Abramov, E.S., Basan, E.S.: Development of a model of a protected cluster–based wireless sensor network. Izvestiya SFedU. Tech. Sci. 12(149), 48–56 (2013)Google Scholar
- 10.Pshikhopov, V.Kh, Soloviev, V.V., Titov, A.E., Finaev, V.I., Shapovalov, I.O.: Group Control of Mobile Objects in Indeterminate Environments. FIZMATLIT, Rostov-on-Don (2015)Google Scholar
- 12.Patel, S.T., Mistry, N.H.: A review: sybil attack detection techniques in WSN. In: Proceedings of 4th International Conference on Electronics and Communication Systems (ICECS), pp. 184–188 (2017)Google Scholar