Now a days it is very important to maintain a
high level security to ensure a safe and trusted
communication of information between various
organizations. But secured data communication over
internet or any other network is always threats of intrusions
and misuses. There are different Soft computing approaches
have been proposed to detect the attacks. In this paper we
proposed the genetic algorithm to generate the rules with
the help of network audit data and for selection of rules used
fitness function. The generated rules are used to detect or
classify the attacks. By using Genetic Algorithm (GA) we
can classify the different types of attack. To implement and
measure the performance of system we used the DARPA
benchmark dataset and obtained reasonable detection rate.
IDS is implemented using GA in two steps. In the first
step, GA is used to generate classification rules where as
in the second step these rules are used for intrusion
detection. This reduces the search space and yields more
accurate results while using smaller population and lesser
number of generations compared to Gong et al.’s
approach. This has reduced the time required for the
generation of fittest rules. The given system is run for
different generations. As the number of generations is increased, more accurate intrusion detection rates are
obtained.
[1] W. Li, “A Genetic Algorithm Approach to Network
Intrusion Detection”, SANS Institute, USA, 2004.
[2] Dheeraj Pal and Amrita Parashar “Improved Genetic
Algorithm for Intrusion Detection System”, 2014 Sixth
International Conference on Computational
Intelligence and Communcation Networks
[3] Li, Wei. 2002. “The integration of security sensors
into the Intelligent Intrusion Detection System (IIDS)
in a cluster environment.” Master’s Project Report.
Department of Computer Science, Mississippi State
University.
[4] MIT Lincoln Laboratory, DARPA datasets, MIT,
USA, in November2004).
http://www.ll.mit.edu/IST/ideval/data/data_index.html
[5] H. Pohlheim, “Genetic and Evolutionary Algorithms:
Principles, Methods and Algorithms”,
http://www.geatbx.com/docu/index.html (accessed in
January 2005).
[6] M. Crosbie and E. Spafford, “Applying Genetic
Programming to Intrusion Detection”, Proceedings of
the AAAI Fall Symposium, 1995
[7] W. Lu and I. Traore, “Detecting New Forms of
Network Intrusion Using Genetic Programming”,
Computational Intelligence, vol. 20, pp. 3, Blackwell
Publishing, Malden, pp. 475-494, 2004.
[8] Weiming Hu, Jun Gao, Yanguo Wang, Ou Wu, and
Stephen Maybank “Online Adaboost-Based
Parameterized Methods for Dynamic Distributed
Network Intrusion Detection”, IEEE Trans. On
Cybernetics.
[9] Zhenwei Yu, Jeffrey J. P. Tsai and Thomas Weigert,
“An Automatically Tuning Intrusion Detection
System”, IEEE Trans. On Systems, Man and
Cybernetics-Part B: Cybernetics Vol.37, No.2, April
2007.
[10] Dong Song, Malcolm I. Heywood and A. Nur Zincir-
Heywood, “Training Genetic Programming on Half a
Million Patterns: An Example from Anomaly
Detection”, IEEE TRANSACTIONS ON
EVOLUTIONARY COMPUTATION, VOL. 9, NO. 3,
JUNE 2005.