IJARME

Semantic Ids Using Wireless Sensor Network

---

---

Abstract

Emerging technologies have metamorphosed the nature of surveillance and monitoring application, but the sensory data collected using various gadgets still remain changeable and poorly synchronized. An event detected by WSN formulates patterns. The sink receives the information about several events happening in the coverage area. Sink has to correlate these streaming data (events) in spatial domain (several sensors) and time domain. This paper proposes a scheme to formulate patterns based on sensing elements and a methodology for detecting an intruder using rule-based semantics. This scheme can be integrated with the surveillance systems to detect the entry of an unauthorized person into a secured area. Real Time implementations prove that events, patterns, rules can efficiently detect an intruder with the help of a wired network with appropriate database. The semantic rules are developed using ANTLR tool.

Recommended Citation

[1] Akyildiz I.F, Weilian Su, Sankarasubramaniam Y, Cayirci E,"A survey on sensor networks", Communications Magazine, IEEE , vol.40, no.8, pp. 102- 114, Aug 2002. [2] Pramod P.J, Srikanth S.V, Vivek N, Patil M.U, Sarat C, "Intelligent Intrusion Detection System (In2DS) using Wireless Sensor Networks", Networking, Sensing and Control, 2009. ICNSC '09. International Conference on, vol., no., pp.587-591, 26-29 March 2009.[3] Sanxian Liu, Ruisheng Zhang, Tongming Wei, Yongying He, Xianrong Su, Lian Li, "Implementing of Gaussian Syntax-Analyzer Using ANTLR", Information Science and Engineering, 2008. ISISE '08. International Symposium on , vol.2, no., pp.749-753, 20-22 Dec. 2008. [4] D. Barbara, J. Couto, S. Jajodia, L. Popyack, and N. Wu, “ADAM: Detect ing intrusions by data mining”, in Proc. IEEE Workshop Inf. Assurance and Security, Jun. 2001, pp. 11–16. [5] K. Julish, “Data mining for intrusion detection: A critical review”, IBM, Kluwer, Boston, MA, Res. Rep. RZ 3398, Feb. 2002. [6] Ilgun K, Kemmerer R.A, Porras P.A, "State transition analysis: a rule-based intrusion detection approach", Software Engineering, IEEE Transactions on, vol.21, no.3, pp.181-199, Mar 1995 [7] Dmitry K, Dmitry V , "An algorithm for rule generation in fuzzy expert systems", Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, vol.1, no., pp. 212- 215 Vol.1, 23-26 Aug. 2004. [8] Ye Changguo, Zhang Qin, Zhou Jingwei, Wei Nianzhong, Zhu Xiaorong, Wang Tailei, "Improvement of Association Rules Mining Algorithm in Wireless Network Intrusion Detection", Computational Intelligence and Natural Computing, 2009. CINC '09. International Conference on , vol.2, no, pp.413-416,6-7June2009. [9] Hui Wang, Guoping Zhang, Huiguo Chen, Xueshu Jiang, "Mining Association Rules for Intrusion Detection", Frontier of Computer Science and Technology, 2009. FCST '09. Fourth International Conference on , vol., no., pp.644-648, 17-19 Dec 2009. [10] Idris N.B, Shanmugam B, "Artificial Intelligence Techniques Applied to Intrusion Detec tion", INDICON, 2005 Annual IEEE , vol., no., pp. 52- 55, 11-13 Dec. 2005.