AEC_GAN: MACHINE LEARNING AND ACGAN-BASED UNBALANCED DATA PROCESSING DECISION-MAKING IN NETWORK ATTACKS
Abstract
Network intrusion detection systems have become an essential part of network infrastructures from hostile activity. Improvements in recent times over machine learning, in general, and ensemble approaches have led to increasing accuracy and dependability. The current overview would try to dissect research work based on ensemble methods, voting and stacking in order to enhance the systems for network intrusion detection. Going through numerous research papers, we intend to hone the focus towards methodology, the algorithms employed, and distinct advantages and disadvantages of them.
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