The article is devoted to the problem of detecting network attacks, both known and
previously unknown. The application of various methods of artificial intelligence in the
scientific literature to solve this problem was analyzed. The advantages of the artificial immune system were revealed. Its main mechanisms including artificial lymphocytes generation,
negative selection, clonal selection, data analysis, and periodic renewal of lymphocytes were
analyzed. The article describes the developed intrusion detection system based on artificial
immune system. Developed system includes a sniffing subsystem, so that allows it to analyze
real data of host network connections. The article also describes network connections dataset
KDD99, which used to efficiency evaluation of developed system. The methods of compressing
the initial dataset proposed in the scientific literature were analyzed, and the drawbacks of
these methods were identified. This article describes the experimental determination of the
network connections significant parameters contained in the dataset. The authors identified
13 significant parameters from 41, and also they described the process of preliminary
processing and preparation of the analyzed data, a series of experiments. The results of the
experiments showed the high efficiency of the developed system in detecting unknown network
attacks, detecting and classifying known attacks.
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