Earticle

초록

영어

A variety of intrusion prevention techniques, such as user authentication using passwords, avoidance of programming errors and information protection, has been used to protect computer systems. However information prevention alone is not sufficient to protect our systems as those systems become even more complex with the rapid growth and expansion of Internet technology and local network systems. Moreover, programming errors, firewall configuration errors and ambiguous or undefined security policies add to the system’s complexity. An Intrusion Detection system (IDS) is therefore needed as another layer to protect computer systems. The IDS is one of the most important techniques of information dynamic security technology. It is defined as a process of monitoring the events occurring in a computer system or network and analyzing them to differentiate between normal activities of the system and behaviors that can be classified as suspicious or intrusive. Current Intrusion Detection Systems have several known shortcomings, such as low accuracy (registering high False Positives and False Negatives); low real-time performance (processing a large amount of traffic in real time); limited scalability (storing a large number of user profiles and attack signatures); an inability to detect new attacks (recognizing new attacks when they are launched for the first time); and weak system reactive capabilities (efficiency of response). This makes the area of IDS an attractive research field. In recent years, researchers have investigated techniques such as artificial intelligence, autonomous agents and distributed systems for detecting intrusion in network environments. In this work we have realized an Intrusion Detection System based on Genetic algorithm (GA) approach. For evolving and testing new rules for intrusion detection system the KDD99Cup training and testing dataset were used.

목차

Abstract
 1. Introduction
 2. Related Works
 3. Genetic Algorithm
 4. Parameters in Genetic Algorithm
  4.1 Fitness Function
  4.2 Crossover and Mutation Operator
 5. Kddcup99 Dataset Description
 6. Experiments and Results
 7. Conclusions
 References

키워드

저자

• S. Selvakani Kandeeban [ Professor and Head, MCA Department, Francis Xavier Engineering College, Tirunelveli, Tamilnadu, India ]
• R. S. Rajesh [ Reader, Department of CSE, Manonmanium Sundaranar University,, Tirunelveli, Tamilnadu, India ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Science and Technology

• 간기

  월간

• pISSN

  2005-4238

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605