One of the solutions used against malicious threats is the Intrusion Detection System (IDS). In addition,
attackers still keep adjusting their instruments and tactics. It is still a difficult job to incorporate an agreed IDS scheme,
however. Several studies have been carried out and tested in this paper to test different machine learning classifiers
based on the KDD intrusion dataset. In order to test the chosen classifiers, multiple output parameters were successfully
computed. In order to increase the intrusion detection system's detection rate, the emphasis was on false negative and
false positive performance indicators. The tests carried out found that the decision table classifier obtained the lowest
false negative score, while the highest average accuracy rating was achieved by the random forest classifier.
Key words: Intrusion detection, Machine Learning, Deep Learning, Network Based Attacks, Denial of Service
Attack
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