An IoT Anomaly Detection Model Based on Artificial Immunity

Abstract:

To resolve the anomaly detection problem in the distributed environment of the Internet of Things (IoT), an artificial immunity-based anomaly detection model for the IoT is proposed in this paper. The proposed model adopts artificial immune mechanisms to recognize anomaly behavior of IoT security threats. It is consisted of Anomaly detection agents (ADA) and Central Service System (CSS). ADA is deployed by the IoT gateway. It collects the initial data of the sense layer of the IoT. It works independently and produces excellent detection elements. It shares its excellent detection elements with the other ones and uploads them to the CSS. Theory analysis shows that the proposed model is able to adapt the local network environment of IoT and improve the anomaly detection ability in the global IoT environment.

• Periodical: Advanced Materials Research (Volumes 424-425)
• Pages: 625-628
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.424-425.625
• Online since: January 2012
• Authors: Cai Ming Liu, Si Yu Chen, Yan Zhang, Run Chen, Kui Liang Guo
• Keywords: Anomaly Detection, Artificial Immune System, Internet of Things (IOT), Security Threat

References

[1] International Telecommunication Union: ITU Internet Reports 2005: The Internet of Things. Geneva: ITU (2005).

[2] Padmavathi, G., Shanmugapriya, D.: A Survey of Attacks, Security Mechanisms and Challenges in Wireless Sensor Networks. International Journal of Computer Science and Information Security, vol. 4 (2009).

[3] Zhou, Y., Fang, Y.G., Zhang, Y.C.: Securing wireless sensor networks: a survey, IEEE Communications Surveys and Tutorials 10, 6-28 (2008).

DOI: 10.1109/comst.2008.4625802

[4] Yang, Y., Wang, X., Zhu, S., Cao, G.: Distributed Software-based Attestation for Node Compromise Detection in Sensor Networks. In: 26th IEEE International Symposium on Reliable Distributed Systems, pp.219-228 (2007).

DOI: 10.1109/srds.2007.31

[5] Mitrokotsa, A., Rieback, M.R. and Tanenbaum, A.S.: Classifying RFID attacks and defenses. Inf. Syst. Front, pp.491-505 (2010).

DOI: 10.1007/s10796-009-9210-z

[6] Mirowski, L., Hartnett, J.: Deckard: A system to detect change of RFID tag ownership. International Journal of Computer Science and Network Security 7, 89-98 (2007).

[7] Xiao, R.B., Wang, L.: Artificial immune system: principle, models, analysis and perspectives, Chinese Journal of Computers 25, 1281-1293 (2002).

[8] Li, T.: Computer immunology. Publishing House of Electronics Industry, Beijing (2004).

[9] Li, T.: An immunity based network security risk estimation. Science in China Series F-Information Sciences, 48, 557-578 (2005).

[10] Kim, J. and Bentley, P.J.: An evaluation of negative selection in an artificial immune system for network intrusion detection. In: the Genetic and Evolutionary Computation Conference (2001).