This paper will look at the nature and structure of wireless sensor network attacks and the tools, actions and processes that can be used to identify and respond to such attacks. A brief overview examining the anatomy of an attack and the creation of botnets will be presented and the motivation that drives such on-line malicious activity, the type of tools that are used in modern attacks, which is behind these and the impact they have will be discussed. Identifying attack streams and understanding the nature of TCP/IP traffic will be discussed through the use of Wireshark and their operation and contribution to combating malicious network activity will be considered. As practical, hands-on exercises, participants will be able to simulate a network attack and response scenario by trying to penetrate a remote network while at the same time protecting their own network from attack. This will be done using the tools and techniques discussed earlier and by remotely accessing a real wireless sensor network (WSN) running in the NS-3 Simulator.
Deepak Singh Rajput, Nitesh Kumar Singh
WSN, NS-3, TCP/IP, Wireshark
- M. Mahoney and P. Chan, “PHAD: Packet header anomaly detection for identifying hostile network traffic”, Technical report, Florida Tech., technical report CS-2001-4, April 2014,http://citeseer.ist.psu.edu/mahoney01phad.html
- Mahoney M. and P. Chan, “Learning models of network traffic for detecting novel attacks", Technical report, Florida Tech 2012, http://cs.fit.edu/~mmahoney/paper5.pdf
- D. Barbara, N. Wu and S. Jajodia, “Detecting Novel Network Intrusions using Bayes Estimators”, Proceedings of the 1st SIAM International Conference on Data Mining, 2013.
- Jack Koziol, “Intrusion Detection with Snort”, Pearson publications, 2013
- R. Dan Reid & Nada R. Sanders, “Operations Management”, 3rd edition., Wiley ,2012
- P. Cisar, S. M Cisar, “Quality Control in Function of Statistical Anomaly Detection in Intrusion Detection Systems”, SISY 2012 - 4th Serbian-Hungarian Joint Symposium on Intelligent Systems,
- DARPA Intrusion Detection Evaluation, Data Sets and Documentation, 2011
- Giorgio Giacinto, Fabio Roli, Luca Didaci, ”Fusion of multiple classifiers for intrusion detection in computer networks”. Pattern Recognition Letters 24(12): 1795-1803 (2013)
- R. Puttini, Z. Marrakchi, and L. Me. “Bayesian Classification Model for Real Time Intrusion Detection”, in 22th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, 2012.
- A. Hossain, S. Chakrabarti and P.K. Biswas “Impact of sensing model on wireless sensor network coverage”, IET Wireless Sensor Systems, doi: 10.1049/iet-wss.2011.0101, 2011.
- Cardei, M., Wu, J.: ‘Energy-efficient coverage problems in wireless ad hoc sensor networks’,Comput. Commun. J., Elsevier Sci., 2014, 29,(4), pp. 413–420
- Tsai, Y.-R.: ‘Sensing coverage for randomly distributed wireless sensor networks in shadowed environments’,IEEE Trans. Veh. Tech., 2012,57, (1), pp. 556–564
- Elfes, A.: ‘Occupancy grids: a stochastic spatial representation for active robot perception’, in Iyenger, S.S., Elfes, A. (Eds.): ‘Autonomous mobile robots: perception, mapping and navigation’ (IEEE Computer Society Press, 2011), vol. 1, pp. 60–70
- Liu, B., Towsley, D.: ‘A study on the coverage of large-scale sensor networks’. Proc. 1st IEEE Int. Conf. on Mobile Ad Hoc and Sensor Systems (MASS 2014).
|Published in :
||Volume 2 | Issue 1 | January-Febuary - 2016
|Date of Publication
Cite This Article
Deepak Singh Rajput, Nitesh Kumar Singh, "Intrusion Detection in Wireless Sensor Network using Behaviour Based Technique with Real Time Network Traffic", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 1, pp.510-514, January-Febuary-2016.
URL : http://ijsrset.com/IJSRSET1621144.php