International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Adaptive Intrusion Detection In Medical Cyber Physical System

Author : Arjun Raj ¹ Sujarani M.S ²

Date of Publication :7th February 2016

Abstract: Medical cyber physical systems (MCPS) are getting popular now a days. Every advanced healthcare hospitals use the help of MCPS to ease otherwise complicated tasks. These systems analyze the patient status using physical sensors and employ corresponding reaction using actuators. An array of sensor devices is attached to the patient which reads real time data and analyses it. Actuators provide corresponding action with respect to the values sensed. Nowadays these cyber physical systems (CPS) are used as tool for cyber attacks. This can relatively harm the patient or may even cause a direct or indirect threat to life. Since the CPS work based on sophisticated and more complex algorithms, intrusion detection in such system can be really complicated task. Since this area is developing in a peak rate, new attacks are being modeled and deployed. Here, intrusion detection system uses behavioral rule specification which is efficient enough to detect unknown attack/attacker patterns. The methodology is to transform behavior rules to corresponding state machines so that the Intrusion detection system can analyze whether its moving towards a safe state(normal behavior) or an unsafe state(deviation from its normal behavior)that compromises the security of the system. This technique also uses a peer to peer approach in which each nodes monitor its neighboring nodes so that to reduce the chance of failure.

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1. K. Park, Y. Lin, V. Metsis, Z. Le, and F. Makedon. Abnormal humanbehavioralpatterndetectioninassistedlivingenviro nments. In 3rd ACM International Conference on Pervasive Technologies Related to Assistive Environments, pages 9:19:8, 2010
2. E. Tapia, S. Intille, and K. Larson. Activity recognition in the home using simple and ubiquitous sensors. In A. Ferscha and F. Mattern, editors, Pervasive Computing, volume 3001 of Lecture Notes in Computer Science, pages 158175. Springer Berlin / Heidelberg, 2004.
3. C.-H.TsangandS.Kwong.Multiagentintrusiondetectionsystem in industrial network using ant colony clustering approach and unsupervised feature extraction. In IEEE International Conference on Industrial Technology, 2005., pages 5156, December 2005.
4. A. Carcano, A. Coletta, M. Guglielmi, M. Masera, I. Fovino, and A. Trombetta. A multidimensional critical state analysis for detecting intrusions Ins cada systems .IEEE Transactionson Industrial Informatics, 7(2):179 186, May 2011.
5. H.AlHamadiandI.R.Chen.Redundancymanagementofmultipa throutingforintrusiontoleranceinheterogeneouswirelesss ensornetworks.IEEETransactionsonNetworkandService Management, 10(2):189203, 2013.
6. I.LeeandO.Sokolsky.Medical cyber physical systems.In47th ACM Design Automation Conference, pages 743748, 2010
7. R. Mitchell and I. R. Chen. Effect of Intrusion Detection and Response on Reliability of Cyber Physical Systems. IEEE Transactions on Reliability, 62(1):199210, March 2013.
8. S.Cheung,B.Dutertre,M.Fong,U.Lindqvist,K.Skinner,an d A. Valdes. Using model-based intrusion detection for SCADA networks. In SCADA Security Scientific Symposium, pages 127134, Miami, FL, USA, January 2007.
9. B. Asfaw, D. Bekele, B. Eshete, A. Villafiorita, and K. Weldemariam. Host-based anomaly detection for pervasive medical systems. In Fifth International Conference on Risks and Security of Internet and Systems, pages 18, October 2010
10. M. Anand, E. Cronin, M. Sherr, M. Blaze, Z. Ives, and I. Lee. Security challenges in next generation cyber physical systems. Beyond SCADA: Networked Embedded Control for Cyber Physical Systems, 2006.
11. A. Cardenas, S. Amin, B. Sinopoli, A. Giani, A. Perrig, and S.Sast ry.Challengesforsecuringcyberphysicalsystems.InFirst Workshop on Cyber-physical Systems Security, DHS, 2009.
12. I. R. Chen and D. C. Wang. Analysis of replicated data with repair dependency. The Computer Journal, 39(9):767779, 1996.
13. M. Aldebert, M. Ivaldi, and C. Roucolle. Telecommunications Demand and Pricing Structure: An Econometric Analysis. Telecommunication Systems, 25:89115, 2004.
14. S. M. Ross. Introduction to Probability Models, 10th Edition. Academic Press, 2009.
15. P. Porras and P. Neumann. EMERALD: Event monitoring enabling responses to anomalous live disturbances. In 20th National Information Systems Security Conference, pages 353365, 1997.
16. F.Bao,I.R.Chen,M.Chang,andJ.H.Cho.HierarchicalTrus t Management for Wireless Sensor Networks and its Applications to Trust Based Routing and Intrusion Detection.IEEE Transactions on Network and Service Management, 9(2):169183, 2012.
17. I. R. Chen and D. C. Wang. Analyzing Dynamic Voting using Petri Nets. In 15th IEEE Symposium on Reliable Distributed Systems, pages 4453, Niagara Falls, Canada, October 1996.C. Hsu. Many popular medical devices may be vulnerable to cyberattacks.http://www.medicaldaily.com/news/20120 410/9486/medical-implants-pacemaker-hackers-cyberattack-fda.htm, April 2012..