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№1, 2022

EXPERIMENTAL STUDY OF MACHINE LEARNING METHODS IN ANOMALY DETECTION

Makrufa Sh. Hajirahimova, Leyla R. Yusifova

hmakrufa@gmail.com, yusifova863@gmail.com

Recently, the widespread usage of computer networks has led to the increase of network threats and attacks. Existing security systems and devices are insufficient in the detection of intruders' attacks on network infrastructure, and they considered to be outdated for storing and analyzing large network traffic data in terms of size, speed, and diversity. Detection of anomalies in network traffic data is one of the most important issues in providing network security. In the paper, we investigate the possibility of using machine learning algorithms in the detection of anomalies – DoS attacks in computer network traffic data on the WEKA software platform. Ensemble model consisting of several unsupervised classification algorithms has been proposed to increase the efficiency of classification algorithms. The effectiveness of the proposed model was studied using the NSL-KDD database. The proposed approach showed a higher accuracy in the detection of anomalies compared to the results shown by the classification algorithms separately (pp.9-19).

Keywords: Big data, Anomaly, DoS attacks, IDS, Machine learning, Ensemble classification
DOI : 10.25045/jpit.v13.i1.02
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