Deep Learning Algorithms for Intrusion Detection Systems: Extensive Comparison Analysis
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Abstract
The network of systems is facing threats from the various attacks, for that a software application needed to regular monitoring the network. Intrusion Detection System (IDS) is application software that monitors the traffic of the network for any unusual activity and raises the alarms when such activity found. The existing IDSs still face troubles in improving the identification accuracy, warning rate not overcoming and identifying mysterious assaults. To take care of the above issues, numerous researcher works going by the researchers have concentrated on creating IDSs that exploit. This survey presents modern approaches in Intrusion Detection System (IDS) applying deep learning models, which have attained great fortune newly, especially in the domain of computer vision, natural language processing, and image processing. In this study, a thorough survey of deep learning methods used by various researches on IDS. This study also proposes a comparison among various deep learning algorithms implemented on KDD Cup, NSL-KDD and UNSW-NS15 data sets.
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