COMPARATIVE ANALYSIS OF MACHINE LEARNING TECHNIQUES FOR NETWORK INTRUSION DETECTION IN CYBER SECURITY WITH A DIVERSE METRIC-BASED PERFORMANCE ASSESSMENT

Abstract

n modern communication and networking, the safe and reliable transfer of data is a necessity of time because the number of intruder attacks on computer networks aims to gain access to crucial information. To protect the network data from any malicious attack, the network intrusion detection systems (NIDSs) play the most critical role. It analyzes the data pattern and secures the network from any attack. This pattern analysis is not possible manually due to the large scale of data; however, machine learning (ML) is a powerful technique to analyze the large scale of data patterns and detect any malicious threats. In this work, we integrated ML with NIDS to analyze and monitor the networking data. We have applied six supervised ML techniques, which include Random, Hoeffding, and Decision Tree, Averaged One-Dependence Estimators, Instance-based KNN, and Naive Bayes, during the experiment and also considered six performance assessment criteria, which include accuracy, precision, true and false positive rates, Matthew correlation coefficient, and receiver operating characteristic area for the three different datasets. The Pareto principle is considered for the training and testing data. According to the results, A1DE is the best model among the applied techniques; it identifies patterns in the data with 99.9964% accuracy, which establishes a foundation for further research.  The researchers use these findings as a starting point for determining which cyber-related attributes should be prioritized to create the most effective and successful NIDS.