WIDS Real-Time Intrusion Detection System Using Entrophical Approach

  • Kamalanaban Ethala
  • R. Sheshadri
  • S. Sibi Chakkaravarthy
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 324)


Nowadays, threats, worms, virus, and malwares in the Internet and security breaches such as intrusion and penetration testing in the network are quite common and lead to the loss of huge amount data. In recent decades, various researchers revealed their perceptions on security and security-related issues. In this paper, we propose a robust intrusion detection system based on Entrophical approach. Here, our system monitors the normal behavior of the network by means of probabilistic system with monitoring active ARP protocol in all PCAP files captured by packet analyzer and detects the intrusion by means of deviation in the PCAP. Entrophical approach deals with profiling strategy; here, data logs of users are classified as profiles such as base, daemon, and user. Various IDS are compared with the Entrophical model-based IDS. Experimental results compared with snort, security onion, and our methodology show that Entrophical model is a level head through many phases, and the comparison outstrips with reliable performance. Real-time results have also been enhanced. This is the first claim for designing an IDS model to combat the real-time attacks such as aircrack-ng, airmon-ng, and airodump-ng from the operating system “BACKTRACK.”


BACKTRACK WLAN Snort IDS Entrophical approach Kali Linux 


  1. 1.
    K. Ethala, R. Sheshadri, Combatting cyber terrorism-assessment of log for malicious signatures. Am. J. Appl. Sci. 1660–1666 (2013) Google Scholar
  2. 2.
    R. Di Pietro, L.V. Mancini, Intrusion Detection Systems. Series: Advances in Information Security 38 (2008)Google Scholar
  3. 3.
    I.A.B. Bazara, H. Anthony Chan, in Handbook of Information and Communication Security. Intrusion detection systems (Springer, Berlin 2010)Google Scholar
  4. 4.
    W. Kanuom, N. Cuppens-Boulahia, F. Cuppens, F. Autrel, Advanced reaction using risk assessment in Intrusion detection system. Crit. Inf. Infrastruct. Secur LNCS 5141, 58–70 (2008)Google Scholar
  5. 5.
    A.A. Ghorbani, W. Lu, M. Tavallaee, Network intrusion detection and prevention. Series: Advances in Information Security (eBook, 2010)Google Scholar
  6. 6.
    N. Tuck, T. Sherwood, B. Calder, G. Varghese, in Deterministic memory-efficient string matching algorithms for intrusion detection. In Proceedings of IEEE INFOCOM (2004), pp. 2628–2639Google Scholar
  7. 7.
    K.A. Garcίa, R. Monroy, L.A. Trejo, C. Mex-Perera, E. Aguirre, Analyzing log files for postmortem intrusion detection. IEEE Trans. Syst. Man Cybern. (2012)Google Scholar
  8. 8.
    C. Cowan, P. Wagle, C. Pu, S. Beattie, J. Walpole, in Buffer overflows: attacks and defenses for the vulnerability of the decade. Proceedings of DARPA Information Survivability Conference Exposition (1999), pp. 154–163Google Scholar
  9. 9.
    Y. Wang, W. Fu, D.P. Agrawal, Intrusion detection in gaussian distributed wireless sensor networks. IEEE (2009)Google Scholar
  10. 10.
    B. Liu, P. Brass, O. Dousse, P. Nain, D. Towsley, in Mobility improves coverage of sensor networks. Proceedings of the 6th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc) (2005)Google Scholar
  11. 11.
    S. Janakiraman, S. Rajasoundaran, P. Narayanasamy, The model—dynamic and flexible intrusion detection protocol for high error rate wireless sensor networks based on data flo. IEEE Google Scholar
  12. 12.
    A. Modirkhazeni, N. Ithnin, O. Ibrahim, in Secure multipath routing protocols in wireless sensor networks: a security survey analysis. IEEE International Conference (2010)Google Scholar
  13. 13.
    Z. Mingqiang, H. Hui, W. Qian, in A graph-based clustering algorithm for anomaly intrusion detection, ICCSE 2012. IEEE Conference (2012)Google Scholar
  14. 14.
    Y. Guan, A.A. Ghorbani, N. Belacel, in Y-means: a clustering method for intrusion detection. Canadian Conference on Electrical and Computer Engineering (2003), p. 14Google Scholar
  15. 15.
    U. Prathap, P. Deepa Shenoy, K.R. Venugopal, in Wireless sensor networks applications and routing protocols: survey and research challenges. IEEE Symposium (2012)Google Scholar
  16. 16.
    G.G. Xie, J. Gibson, in A network layer protocol for UANs to address propagation delay induced performance limitations. Proceedings of the MTS/IEEE Conference and Exhibition (OCEANS 2001). (2011) 20872094Google Scholar
  17. 17.
    M. Patil, R.C. Biradar, in A survey on routing protocols in wireless sensor networks, ICON 2012. IEEE Conference (2012)Google Scholar
  18. 18.
    Q. Ren, Q. Liang, in A contention-based energy-efficient MAC protocol for wireless sensor networks. Proceedings of 2006 IEEE Wireless Communications and Networking Conference (WCNC 06). (2006), pp. 1154–1159Google Scholar
  19. 19.
    W. Zhu, Q. Wang, in Improving intrusion detection through merging heterogeneous IP data. Proceeding of the IEEE International Conference on Information and Automation Shenyang (2012)Google Scholar
  20. 20.
    B. Zhang, Research on intrusion detection based on heuristic genetic neural network. Adv. Intell. Soft Comput. 149, 567–573 (2012)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  • Kamalanaban Ethala
    • 1
  • R. Sheshadri
    • 2
  • S. Sibi Chakkaravarthy
    • 1
  1. 1.Department of Computer Science and EngineeringVel Tech UniversityChennaiIndia
  2. 2.Department of Computer Science and EngineeringSri Venkateswara UniversityTirupathiIndia

Personalised recommendations