An Effective Approach for Improving Anomaly Intrusion Detection

Authors

  • Kumar JS Department of CSE, DMS SVH College of Engg. , Machilipatnam ,AP
  • Appa Rao SSV Department of CSE, VISIT Engg. College , Tadepalliguem ,AP
  • Subha Sree M Department of CSE, DMS SVH College of Engg. , Machilipatnam ,AP

Keywords:

Intrusion Detection System, Layered approach, Clustering, FAM

Abstract

Intrusion Detection Systems (IDS) is a key part of system defense, where it identifies abnormal activities happening in a computer system. In general, the traditional intrusion detection relies on the extensive knowledge of security experts, in particular, on their familiarity with the computer system to be protected. To reduce this dependence, various data-mining, soft-computing and machine learning techniques have been proposed in recent years for the development of better intrusion detection systems. Many researchers used Conditional Random Fields and Layered Approach for purpose of intrusion detection. They also demonstrated that high attack detection accuracy can be achieved by using Conditional Random Fields and high efficiency by implementing the Layered approach. In the paper we explained a new method called fuzzy ARTMAP classifier (FAM) and clustering technique for effectively identifying the intrusion activities within a network. Processing huge data would make the system error prone, hence clustering the data into groups and then processing will result in having a better system. From each of the cluster, representative data is selected in the selective process for further processing. For classification process, layered fuzzy ARTMAP will have the better results when compared to other normal classifier algorithms. Finally the experiments and evaluations of the proposed intrusion detection system is using the KDD Cup 99 intrusion detection data set.

References

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Published

2025-11-10

How to Cite

[1]
J. Kumar, S. Appa Rao, and M. Subha Sree, “An Effective Approach for Improving Anomaly Intrusion Detection”, Int. J. Comp. Sci. Eng., vol. 3, no. 10, pp. 92–98, Nov. 2025.

Issue

Vol. 3 No. 10 (2015): IJCSE October Edition

Section

Review Article

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