A Survey on Anomaly-Based Network Intrusion Detection Systems

Authors

  • Neeraj Shukla  Gyan Ganga College of Technology, Jabalpur, Madhya Pradesh, India
  • Anjali Vishwakarma   Gyan Ganga College of Technology, Jabalpur, Madhya Pradesh, India

Keywords:

Computer networks, Network security, Anomaly detection, Intrusion detection

Abstract

The significance of system security has become enormously and various gadgets have been acquainted with enhance the security of a system. System Intrusion Detection Systems (NIDS) are among the most broadly sent such framework. Prevalent NIDS utilize an accumulation of marks of known security dangers and infections, which are utilized to sweep every parcel's payload. Most IDSs do not have the capacity to identify novel or beforehand obscure assaults. An uncommon sort of IDSs, called Anomaly Detection Systems, create models taking into account typical framework or system conduct, with the objective of recognizing both known and obscure assaults. Oddity location frameworks face numerous issues including high rate of false alert, capacity to work in online mode, and versatility. This paper shows a specific study of incremental methodologies for recognizing abnormality in ordinary framework and system activity.

References

  1. Alex Lam (2014), “New IPS to Boost Security, Reliability and Performance of the Campus Network”, Newsletter of Computing Services Center.
  2. Pfahringer B (2013), “Winning the KDD99 Classification Cup: Bagged Boosting”, In SIGKDD Explorations.
  3. Barbara D, Domeniconi C and Rogers J (2014), “Detecting Outliers Using Transduction And Statistical Testing”, Association for Computing Machinery.
  4. Dasgupta D(2011),“AnArtificialImmune System AsAMultiagent Decision Support System”, IEEE International Conferenceon Systems, Manand Cybernetics, pp. 3816-3820.
  5. Reuters News Service (2005), FBI Agents bust ‘Botmaster’, November 4.
  6. Jelena Mirkovic, Sven Dietrich, David Dittrich and Peter Reiher (2005), Internet Denial of Service: Attack and Defense Mechanisms, Prentice Hall PTR, ISBN 0131475738.
  7. Ma J and Perkins S (2003), “Online Novelty Detection on Temporal Sequences”, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Washington, DC.
  8. Levin (2000), “KDD-99 Classifier Learning Contest: LLSoft’s Results Overview”, SIGKDD Explorations.
  9. LI Yongzhong, YANG Ge and XU Jing Zhao Bo (2008,) “A New Intrusion Detection Method Based on Fuzzy HMM”, IEEE, Vol. 2, No. 8.
  10. Ihler A, Hutchins J and Smyth P (2006), “Adaptive Event Detection with Time-varying Poisson Processes”, ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD), Philadelphia, PA.
  11. Sharma S K, Pandey P and Tiwar S K (2012), “An Improved Network Intrusion Detection Technique Based On k-means Clustering Via Naïve Bayes Classification”, IEEE, Vol. 2, No. 2.
  12. Tarem Ahmed, Boris Oreshkin and Mark Coates (2007), “Machine Learning Approaches to Network Anomaly Detection”, in Workshop on Tackling Computer Systems Problems with Machine Learning Techniques, McGill University Montreal, QC, Canada.
  13. Vaughn Randal and Evron Gadi (2007), “DNS Amplification Attacks”.
  14. Zhenglie Li (2011) “Anomaly Intrusion Detection Method Based on K-Means Clustering Algorithm with Particle Swarm Optimization”, Springer, Vol. 4, No. 2.

International Journal of Scientific Research in Science, Engineering and Technology

Downloads

Published

2016-02-25

Issue

Volume 2, Issue 1, January-February-2016

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Neeraj Shukla, Anjali Vishwakarma , " A Survey on Anomaly-Based Network Intrusion Detection Systems , International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 1, pp.300-306, January-February-2016.