Abstract
With theincreasing worldwide network attacks, intrusion detection (ID) hasbecome a popularresearch topic inlast decade.Several artificial intelligence techniques such as neural networks and fuzzy logichave been applied in ID. The results are varied. Theintrusion detection accuracy is themain focus for intrusion detection systems (IDS). Most research activities in the area aiming to improve the ID accuracy. In this paper, anartificial immune system (AIS) based network intrusion detection scheme is proposed. An optimized feature selection using Rough Set (RS) theory is defined. The complexity issue is addressed in the design of the algorithms. The scheme is tested on the widely used KDD CUP 99 dataset. The result shows that theproposed scheme outperforms other schemes in detection accuracy.
Highlights
Driven by the rapid growth of the computer network technologies, the security of the computer and network information is becoming increasingly important
The intrusion detection system (IDS) is such a system, which is composed by a series of devices and software applications to monitor network activities in order to protect the system from malicious activities
Artificial Immune System (AIS) applies to various areas of researches that attempt to build a bridge between immunology and engineering by using the techniques of mathematical and computational modeling of immunology
Summary
Driven by the rapid growth of the computer network technologies, the security of the computer and network information is becoming increasingly important. Bio-inspired algorithms have been studied and applied in intrusion detection [5] aiming for better performance Algorithms such as Genetic Algorithm (GA), Artificial Neural Networks (ANN) and Artificial Immune Systems are widely studied. A Multilayer IDS using AIS was proposed by Dasgupta [9] in order to provide systematic defense These AIS based IDS have achieved good detection results. Their computing complexity is quite high due to the complicated feature comparing. Ourstudy on AIS based IDS is to further improve its detection accuracy while keeping a low algorithm complexity. An improved AIS based intrusion detection system with Rough Set feature selection algorithm is presented. The anomaly detection in the system is set up based on AIS negative selection algorithm.
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