Eunmok Yang1, Gyanendra Prasad Joshi2, Changho Seo3,*
CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1647-1663, 2021, DOI:10.32604/cmc.2020.013210
Abstract In network-based intrusion detection practices, there are more regular instances than intrusion instances. Because there is always a statistical imbalance in the instances, it is difficult to train the intrusion detection system effectively. In this work, we compare intrusion detection performance by increasing the rarely appearing instances rather than by eliminating the frequently appearing duplicate instances. Our technique mitigates the statistical imbalance in these instances. We also carried out an experiment on the training model by increasing the instances, thereby increasing the attack instances step by step up to 13 levels. The experiments included not only known attacks, but also… More >
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