Layered Approach for Intrusion Detection System Using Hidden Conditional Random Fields

Authors(1) :-M. Mangaleswaran

Intrusion detection is a vital approach to guarantee the security of computers and networks. In this paper, a new intrusion detection framework is proposed in view of Hidden Conditional Random Fields. With a specific end goal to enhance the execution of HCRFs, we present the Two-organize Feature Selection strategy, which contains Manual Feature Selection technique and Backward Feature Elimination Wrapper technique. The BFEW is a perspective determination strategy which is presented in light of wrapper approach. Experimental results on KDD99 dataset demonstrate that the proposed IDS not just have an extraordinary favourable position in identification effectiveness additionally have a higher exactness. In this paper we built up a handy test suite for showing signs of improvement the ability and accuracy of an interruption discovery framework utilize the layered CRFs. We set up changed sorts of checks at a few levels in each layer .Our structure look at different quality at each layer with a specific end goal to successfully group any encroach of security. Once the assault is identified, it is hinted through cell phone to the framework manager for protection the server framework. We set up tentatively that the layered CRFs can in this way be more expert in identifying interruptions.

Authors and Affiliations

M. Mangaleswaran
Department of Computer Science and Engineering, Jansons Institute of Technology, Coimbatore, Tamil Nadu, India

Intrusion detection system; Hidden conditional Random Fields; Conditional random fields; Anomalous Activity; Layer-based Intrusion Detection System.

  1. KK Gupta, B Nath and K Ramamohanarao, "Layered approach using conditional random fields for intrusion detection", IEEE Transactions on Dependable and Secure Computing, vol. 7, no. 1, (2010), pp. 35-49.
  2. S Mukherjeea and N Sharma, "Intrusion Detection using Naive Bayes Classifier with Feature Reduction", Procedia Technology, vol. 4, (2012), pp. 119-128.
  3. SJ Horng, MY Su, YH Chen, TW Kao, RJ Chen, JL Lai and CD Perkasa, "A novel intrusion detection system based on hierarchical clustering and support vector machines", Expert Systems with Applications, vol. 38, (2011), pp. 306-313.
  4. J Vlcek and L Luksan, "Generalizations of the limited-memory BFGS method based on the quasiproduct form of update", Journal of Computational and Applied Mathematics, vol. 241, (2013), pp. 116- 129.
  5. Li, J Xia, S Zhang, J Yan, X Ai and K Dai, "An efficient intrusion detection system based on support vector machines and gradually feature removal method", Expert Systems with Applications, vol. 39, no. 1, (2012), pp. 24-430.
  6. V Bolón-Canedo, N Sánchez-Maroño and A Alonso-Betanzos, "Feature selection and classification in multiple class datasets: an application to KDD Cup 99 dataset", Expert Systems with Applications, vol. 38, no. 5, (2011), pp. 5947- 5957.
  7. W Alsharafat, "Applying Artificial Neural Network and eXtended Classifier System for Network Intrusion Detection", The International Arab Journal of Information Technology, vol. 10, no. 3, (2013), pp. 230-238.
  8. L Zhang, LG Meng and CJ Hou, "Intrusion Detection Based on Immune Principles and Fuzzy Association Rules", Intelligence Computation and Evolutionary Computation, vol. 180, (2013), pp. 31- 35.
  9. C Guo, YJ Zhou, Y Ping, ZK Zhang, GL Liu and YX Yang, "A distance sum-based hybrid method for intrusion detection". Appl Intell, vol. 40, (2014), pp. 178-188.
  10. SANS Institute, (2012) Intrusion Detection FAQ. Autonomous Agents for Intrusion Detection, http://www.cerias., 2010.
  11. Kapil Kumar Gupta, Baikunth Nath, Senior Member, IEEE, and Ramamohanarao Kotagiri, Member, IEEE, ?Layered Approach Using Conditional Random Fields for Intrusion Detection?, ieee transactions on dependable and secure computing, vol. 7, no. 1, January -march 2010
  12. CRF++: Yet another CRF Toolkit,, 2010.

Publication Details

Published in : Volume 3 | Issue 5 | July-August 2017
Date of Publication : 2017-08-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 65-69
Manuscript Number : IJSRSET173412
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

M. Mangaleswaran, " Layered Approach for Intrusion Detection System Using Hidden Conditional Random Fields, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 3, Issue 5, pp.65-69, July-August-2017.
Journal URL :

Article Preview