High Impact Factor : 4.396 icon | Submit Manuscript Online icon |

Improved Genetic Algorithm for Intrusion Detection System

Author(s):

Dimpi Patel , Hasmukh Goswami College of Engineering; Prof. Indr Jeet Rajput, Hasmukh Goswami College of Engineering

Keywords:

Intrusion Detection System (IDS), network Intrusion detection system (NNIDS), Genetic algorithm (GA), Detection rate (DR), False Positive (FP)

Abstract

The Internet has become a part of daily life and an essential tool today. Internet has been used as an important component of business models. Therefore, It is very important to maintain a high level security to ensure safe and trusted communication of information between various organizations. Intrusion detection is one of the important security constraints for maintaining the integrity of information. Various approaches have been applied in past that are less effective to curb the menace of intrusion. There are large amount of network traffic captured in terms of number of features and number of record, so it is very difficult to process all the network traffic before making any decision about normal or abnormal. So it is having longer training time and complexity. Thus the purpose is to provide an intrusion detection system (IDS), by modifying the genetic algorithm to network intrusion detection system. As we have applied attribute subset reduction on the basis of Information gain. So the training time and complexity reduced considerably. we embedded a soft computing approach in rule generation, so Generated rule can detect attack with more efficiency.

Other Details

Paper ID: IJSRDV3I40630
Published in: Volume : 3, Issue : 4
Publication Date: 01/07/2015
Page(s): 1174-1177

Article Preview

Download Article