カテゴリ: 論文誌(論文単位)グループ名: 【C】電子・情報・システム部門発行日: 2020/06/01タイトル(英語): A Deep Learning Approach Based on Sparse Autoencoder with Long Short-Term Memory for Network Intrusion Detection著者名: Zolzaya Kherlenchimeg (Department of Design and Media Technology, Graduate School of Engineering, Iwate University), Naoshi Nakaya (Department of Design and Media Technology, Graduate School of Engineering, Iwate University)著者名(英語): Zolzaya Kherlenchimeg (Department of Design and Media Technology, Graduate School of Engineering, Iwate University), Naoshi Nakaya (Department of Design and Media Technology, Graduate School of Engineering, Iwate University)キーワード: intrusion detection，sparse autoencoder，long short-term memory，dimension reduction，deep learning要約(英語): In recent years, a deep neural network has been solving a variety of complex problems of science and engineering fields ranging from healthcare to transportation. Among them, one of the most crucial issues is to protect a network against cyber threats. In this article, we present a two-stage IDS framework based on a single-layer Sparse Autoencoder (SAE) and Long Short-Term Memory (LSTM), to design an effective network intrusion detection. Initially, the single-layer SAE learns new feature representations of the data through the nonlinear mapping, following that, the new feature representations are fed into the LSTM model to classify network traffic whether it is being normal or attack. The proposed framework was evaluated on the benchmark NSL-KDD dataset, where the mean accuracy of the proposed method was achieved 84.8%. The experimental results show that the two-stage IDS framework achieved better classification accuracy than the existing state-of-the-art methods.本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.140 No.6 （2020） 特集：来るべきIoT社会に向けた情報通信技術本誌掲載ページ: 592-599 p原稿種別: 論文／英語電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/140/6/140_592/_article/-char/ja/
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