Performance evaluation of cyber-physical intrusion detection on a robotic vehicle

Proceedings - 15th IEEE International Conference on Computer and Information Technology, CIT 2015, 14th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2015, 13th IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2015 and 13th IEEE International Conference on Pervasive Intelligence and Computing, PICom 2015

Volumn , Issue , 2015, Pages 2106-2113

  Vuong, Tuan Phan ^a   Loukas, George ^a   Gan, Diane ^a  

^a UNIVERSITY OF GREENWICH   (United Kingdom)

Author keywords

Command injection; Cyber physical attack; Cyber physical systems; Decision tree; Denial of service (DoS); Detection latency; Intrusion detection; Malware; Mobile robots; Network security

Indexed keywords

DECISION TREES; DENIAL-OF-SERVICE ATTACK; EMBEDDED SYSTEMS; ENERGY UTILIZATION; FEATURE EXTRACTION; INTRUSION DETECTION; MALWARE; MERCURY (METAL); MOBILE ROBOTS; NETWORK SECURITY; ROBOTICS; TRANSMISSION CONTROL PROTOCOL; UBIQUITOUS COMPUTING;

COMMAND INJECTIONS; CYBER PHYSICAL SYSTEMS (CPSS); CYBER PHYSICALS; DENIAL OF SERVICE; DETECTION LATENCY;

COMPUTER CRIME;

EID: 84964257923     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CIT/IUCC/DASC/PICOM.2015.313     Document Type: Conference Paper

References (25)

1. G. Loukas, Cyber-Physical Attacks: A Growing Invisible Threat. Butterworth-Heinemann (Elsevier), 2015.
2. G. Jennings, "Iran claims to have flown reverse-engineered us stealth uav", November 09, 2014. London - IHS Jane's Defence Weekly.
3. R. Sasi, "Maldrone the first backdoor for drones", January 26, 2015. Fb1h2s aka Rahul Sasi's Blog.
4. S. Checkoway, D. McCoy, B. Kantor, D. Anderson, H. Shacham, S. Savage, K. Koscher, A. Czeskis, F. Roesner, and T. Kohno, "Comprehensive experimental analyses of automotive attack surfaces", in Usenix Security Symposium, 2011.
5. A. J. Kerns, D. P. Shepard, J. A. Bhatti, and T. E. Humphreys, "Unmanned aircraft capture and control via GPS spoofing", Journal of Field Robotics, vol. 31, pp. 617-636, 2014.
6. J. Griffiths, "Zhejiang University team scoops 10, 600 Yuan for hacking into Tesla Model S", July 17, 2014. South China Morning Post.
7. T. Vuong, A. Filippoupolitis, G. Loukas, and D. Gan, "Physical indicators of cyber attacks against a rescue robot", in IEEE International Conference on Pervasive Computing and Communications, pp. 338-343, IEEE, 2014.
8. R. Mitchell and I.-R. Chen, "A survey of intrusion detection techniques for cyber-physical systems", ACM Computing Surveys (CSUR), vol. 46, no. 4, p. 55, 2014.
9. A. S. Uluagac, V. Subramanian, and R. Beyah, "Sensory channel threats to cyber physical systems: A wake-up call", in 2014 IEEE Conference on Communications and Network Security (CNS), pp. 301-309, IEEE, 2014.
10. T. Bonaci, J. Herron, T. Yusuf, J. Yan, T. Kohno, and H. J. Chizeck, "To Make a Robot Secure: An Experimental Analysis of Cyber Security Threats Against Teleoperated Surgical Robotics", pp. 1-11, 2015.
11. S. Shetty, T. Adedokun, and L.-H. Keel, "Cyberphyseclab: A testbed for modeling, detecting and responding to security attacks on cyber physical systems", 2014.
12. A. Fagiolini, G. Dini, and A. Bicchi, "Distributed intrusion detection for the security of industrial cooperative robotic systems", in World Congress, vol. 19, pp. 7610-7615, 2014.
13. R. Mitchell and I.-R. Chen, "Adaptive Intrusion Detection of Malicious Unmanned Air Vehicles Using Behavior Rule Specifications", IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. PP, no. 99, p. 1, 2013.
14. V. Skormin, A. Dolgikh, and Z. Birnbaum, "The behavioral approach to diagnostics of cyber-physical systems", in AUTOTESTCON, 2014 IEEE, pp. 26-30, IEEE, 2014.
15. K. Sravani and P. Srinivasu, "Comparative study of machine learning algorithm for intrusion detection system", in Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2013, pp. 189-196, Springer, 2014.
16. G. Kim, S. Lee, and S. Kim, "A novel hybrid intrusion detection method integrating anomaly detection with misuse detection", Expert Systems with Applications, vol. 41, no. 4, pp. 1690-1700, 2014.
17. B. R. Patel and K. K. Rana, "A survey on decision tree algorithm for classification", International Journal of Engineering Development and Research, vol. 2, 2014.
18. S. Noh, C. Lee, K. Choi, and G. Jung, "Detecting distributed denial of service (ddos) attacks through inductive learning", Intelligent Data Engineering and Automated Learning, pp. 286-295, 2003.
19. A. Filippoupolitis, G. Loukas, and S. Kapetanakis, "Towards real-time profiling of human attackers and bot detection", in Proceedings of 7th International Conference on Cybercrime Forensics Education and Training (CFET), 2014.
20. Watts up? meters, www.wattsupmeters.com.
21. M. Kuhn, W. Steve, and N. Coulter, "Package C50", 2014.
22. T. Sing, O. Sander, N. Beerenwinkel, and T. Lengauer, "Rocr: visualizing classifier performance in r", Bioinformatics, vol. 21, no. 20, pp. 3940-3941, 2005.
23. M. Striki, K. Manousakis, D. Kindred, D. Sterne, G. Lawler, N. Ivanic, and G. Tran, "Quantifying resiliency and detection latency of intrusion detection structures", in Military Communications Conference, 2009. MILCOM 2009. IEEE, pp. 1-8, IEEE, 2009.
24. T.-L. Chin and W.-C. Chuang, "Latency of collaborative target detection for surveillance sensor networks", Parallel and Distributed Systems, IEEE Transactions on, vol. 26, no. 2, pp. 467-477, 2015.
25. G. Loukas, D. Gan, and T. Vuong, "A review of cyber threats and defence approaches in emergency management", Future Internet, vol. 5, no. 2, pp. 205-236, 2013.