Continuous Features Discretization for Anomaly Intrusion Detectors Generation
This work addresses network security challenges for intrusion detection systems, but it appears incremental as it builds on existing genetic algorithm and discretization methods.
The paper tackles the problem of generating anomaly network intrusion detectors by using a genetic algorithm combined with discretization of continuous features to improve homogeneity across data types, and reports good results when tested on the NSL-KDD dataset with various distance methods.
Network security is a growing issue, with the evolution of computer systems and expansion of attacks. Biological systems have been inspiring scientists and designs for new adaptive solutions, such as genetic algorithms. In this paper, we present an approach that uses the genetic algorithm to generate anomaly net- work intrusion detectors. In this paper, an algorithm propose use a discretization method for the continuous features selected for the intrusion detection, to create some homogeneity between values, which have different data types. Then,the intrusion detection system is tested against the NSL-KDD data set using different distance methods. A comparison is held amongst the results, and it is shown by the end that this proposed approach has good results, and recommendations is given for future experiments.