PERFORMANCE EVALUTION OF SOFT COMPUTING TECHNIQUE BASED INTRUSION DETECTION SYSTEM.

Authors

  • Akanksha verma Department of Computer Science and Engineering, DIMAT Raipur (C.G.)
  • Reader and Head Preeti Tuli Department of Computer Science and Engineering, DIMAT Raipur (C.G.)

Keywords:

IDS; Genetic algorithm; Neural networks; PSO

Abstract

Intrusion detection is the act of detecting undesirable activity on a system or a gadget. Labeled datasets play a major role as the
process of validating and evaluating a machine learning techniques in intrusion detection systems through the survey we adopt
NSL-KDD dataset (an improve version of KDD’99). Usually these data contain lots of irrelevant or redundant features. To
improve the efficiency of IDS, relevant features are necessary to be extracted from original data onto feature selection
approaches. In this paper, the genetic algorithm, neural network and PSO are analyzed deeply. This research paper presents the
thorough survey of algorithms for detection of unwanted traffic on a network.

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Published

2015-08-25

How to Cite

Akanksha verma, & Reader and Head Preeti Tuli. (2015). PERFORMANCE EVALUTION OF SOFT COMPUTING TECHNIQUE BASED INTRUSION DETECTION SYSTEM. International Journal of Advance Research in Engineering, Science & Technology, 2(8), 92–97. Retrieved from https://ijarest.org/index.php/ijarest/article/view/281

Issue

Vol. 2 No. 8 (2015): Volume: 02, Issue: 08 - August, 2015.

Section

Articles