Thavavel Vaiyapuri*, Adel Binbusayyis
CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3271-3288, 2021, DOI:10.32604/cmc.2021.017665
Abstract In the era of Big data, learning discriminant feature representation from network traffic is identified has as an invariably essential task for improving the detection ability of an intrusion detection system (IDS). Owing to the lack of accurately labeled network traffic data, many unsupervised feature representation learning models have been proposed with state-of-the-art performance. Yet, these models fail to consider the classification error while learning the feature representation. Intuitively, the learnt feature representation may degrade the performance of the classification task. For the first time in the field of intrusion detection, this paper proposes an unsupervised IDS model leveraging the… More >
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