Abstract
Feature reduction plays an important role in intrusion detection system. The large amount of feature in network as well as host data effect the performance of intrusion detection method. Various authors are research proposed a method of intrusion detection based on machine learning approach and neural network approach, but all of these methods lacks in large number of feature attribute in intrusion data. In this paper we discuss its various method of feature reduction using artificial immune system and neural network. Artificial immune system is biological inspired system work as mathematical model for feature reduction process. The neural network well knows optimization technique in other field. In this paper we used neural network as feature reduction process. The feature reduction process reduces feature of intrusion data those are not involved in security threats and attacks such as TCP protocol, UDP protocol and ICMP message protocol. This reduces feature-set of intrusion improve the classification rate of intrusion detection and improve the speed performance of the intrusion detection system. The current research going on fixed and static number of feature reduction, we proposed an automatic and dynamic feature reduction technique using PCNN network.
Highlights
Due to the rapid increase in the illicit network activities, intrusion detection system (IDS) as a component of defense-indepth is very necessary because traditional firewall techniques cannot provide complete protection against intrusion[1]
human immune system (HIS) protects the body from damage caused by a large protects the body from damage caused by a large number of harmful bacteria and viruses caused, and provides the body with a high degree of protection against invading pathogens, to a robust manner, itself organized and distributed
We discuss some related work to intrusion detection and feature reduction based on artificial immune system and neural network
Summary
Due to the rapid increase in the illicit network activities, intrusion detection system (IDS) as a component of defense-indepth is very necessary because traditional firewall techniques cannot provide complete protection against intrusion[1]. The feature space with limited features that really contributes to the classification which reduce the cost of pre-processing and minimizes the impact of "peak" phenomenon in the classification[9] Through this improve the overall performance of intrusion detection systems based on classifiers. Artificial neural network (ANN) is a new data-mining approach in intrusion detection on an ANN comprises a number of processing elements which are strongly connected to each other, and transformation of a set of inputs to a set of expected results. Neural networks are both in the detection of intrusion into the abnormality detection signal and the penetration of abuse used.
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