Advertisement

Hybrid Network Intrusion Detection Systems: A Decade’s Perspective

  • Asish Kumar Dalai
  • Sanjay Kumar Jena
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 395)

Abstract

With the increasing deployment of network systems, network attacks are increasing in intensity as well as complexity. Along with these increasing network attacks, many network intrusion detection techniques have been proposed which are broadly classified as being signature-based, classification-based, or anomaly-based. A deployable network intrusion detection system (NIDS) should be capable of detecting of known and unknown attacks in near real time with very low false positive rate. Supervised approaches for intrusion detection provides good detection accuracy for known attacks, but they can not detect unknown attacks. Some of the existing NIDS emphasize on unknown attack detection by using unsupervised anomaly detection techniques, but they can not distinguish network data as accurately as supervised approaches. Moreover they do not consider some other important issues like real time detection or minimization of false alarm. To overcome these problems, in the recent years many hybrid NIDS have been proposed which are basically aimed at detecting both known and unknown attacks with high accuracy of detection. In this literature review on hybrid network intrusion detection systems, we will discuss a few of the notable hybrid NIDS proposed in the recent years and will try to provide a comparative study on them.

Keywords

Intrusion detection system NIDS Network security 

References

  1. 1.
    Chih-Fong Tsai, C.Y.L.: A triangle area based nearest neighbors approach to intrusion detection. Pattern Recognition 43 222–229 2010Google Scholar
  2. 2.
    Wun-Hwa Chen, Sheng-Hsun Hsu, H.P.S.: Application of svm and ann for intrusion detection. Computers and Operations Research 32 2617–2634 2005Google Scholar
  3. 3.
    Alvaro Herrero, Emilio Corchado, M.A.P.A.A.: Movih-ids: A mobile-visualization hybrid intrusion detection system. Neurocomputing 72 2775–2784 2009Google Scholar
  4. 4.
    Tansel Ozyer, Reda Alhajj, K.B.: Intrusion detection by integrating boosting genetic fuzzy classier and data mining criteria for rule pre-screening. Journal of Network and Computer Applications 30 99–113 2007Google Scholar
  5. 5.
    Sandhya Peddabachigari, Ajith Abrahamb, C.G.J.T.: Modeling intrusion detection system using hybrid intelligent systems. Journal of Network and Computer Applications 30 114–132 2007Google Scholar
  6. 6.
    M Panda, Ajith Abraham, M.R.P.: A hybrid intelligent approach for network intrusion detection. In: Proc. International Conference on Communication Technology and System Design 2011. ICCTSD 1–9 2011Google Scholar
  7. 7.
    Baojun Zhang, Xuezeng Pan, J.W.: Hybrid intrusion detection system for complicated network. In: Proc. of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery. FSKD 2007Google Scholar
  8. 8.
    J. Gomez, C. Gil, N.P.R.B.C.J.: Design of a snort-based hybrid intrusion detection system. In: Proc.of the IWANN 2009. 515–522 2009Google Scholar
  9. 9.
    Jawhar, M., Mehrotra, M.: Design network intrusion detection system using hybrid fuzzy-neural network. International Journal of Computer Science and Security 4 285 2010Google Scholar
  10. 10.
    Aydın, M., Zaim, A., Ceylan, K.: A hybrid intrusion detection system design for computer network security. Computers & Electrical Engineering 35 (2009) 517–526 2009Google Scholar
  11. 11.
    Hwang, K., Cai, M., Chen, Y., Qin, M.: Hybrid intrusion detection with weighted signature generation over anomalous internet episodes. IEEE Transactions on Dependable and Secure Computing, 4 (2007) 41–55Google Scholar
  12. 12.
    Yuk Ying Chung and Noorhaniza Wahid. A hybrid network intrusion detection system using simplified swarm optimization (sso). Applied Soft Computing, 12(9):3014–3022, 2012.Google Scholar
  13. 13.
    Reda M Elbasiony, Elsayed A Sallam, Tarek E Eltobely, and Mahmoud M Fahmy. A hybrid network intrusion detection framework based on random forests and weighted k-means. Ain Shams Engineering Journal, 4(4):753–762, 2013.Google Scholar
  14. 14.
    Gisung Kim, Seungmin Lee, and Sehun Kim. A novel hybrid intrusion detection method integrating anomaly detection with misuse detection. Expert Systems with Applications, 41(4):1690–1700, 2014.Google Scholar
  15. 15.
    Bin Luo and Jingbo Xia. A novel intrusion detection system based on feature generation with visualization strategy. Expert Systems with Applications, 41(9):4139–4147, 2014.Google Scholar

Copyright information

© Springer India 2017

Authors and Affiliations

  1. 1.National Institute of Technology RourkelaRourkelaIndia

Personalised recommendations