Intrusion detection systems using classical machine learning techniques versus integrated unsupervised feature learning and deep neural network

Authors: Shisrut Rawat, Aishwarya Srinivasan, Vinayakumar R
Published: 2019-10-01

Source: https://arxiv.org/abs/1910.01114

PDF: https://arxiv.org/pdf/1910.01114

AIにより推定されたラベル

モデルの設計や精度 機械学習アルゴリズム 攻撃の評価

※ こちらのラベルはAIによって自動的に追加されました。そのため、正確でないことがあります。
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Abstract

Security analysts and administrators face a lot of challenges to detect and prevent network intrusions in their organizations, and to prevent network breaches, detecting the breach on time is crucial. Challenges arise while detecting unforeseen attacks. This work includes a performance comparison of classical machine learning approaches that require vast feature engineering, versus integrated unsupervised feature learning and deep neural networks on the NSL-KDD dataset. Various trials of experiments were run to identify suitable hyper-parameters and network configurations of machine learning models. The DNN using 15 features extracted using Principal Component analysis was the most effective modeling method. The further analysis using the Software Defined Networking features also presented a good accuracy using Deep Neural network.