淡江大學機構典藏:Item 987654321/120147
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    題名: An intrusion detection model using improved convolutional deep belief networks for wireless sensor networks
    作者: Weimin Wen;Cuijuan Shang;Zaixiu Dong;Huan-Chao Keh;Diptendu Sinha Roy
    關鍵詞: intrusion detection;improved convolutional deep belief networks;redundancy detection;deeply compressed algorithm;wireless sensor networks;WSNs
    日期: 2021-01-28
    上傳時間: 2021-03-11 12:12:49 (UTC+8)
    摘要: Intrusion detection is a critical issue in the wireless sensor networks (WSNs), specifically for security applications. In literature, many classification algorithms have been applied to address the intrusion detection problems. However, their efficiency and scalability still need to be improved. This paper proposes an improved convolutional deep belief network-based intrusion detection model (ICDBN_IDM), which consists of a redundancy detection algorithm based on the convolutional deep belief network and a performance evaluation strategy. The redundancy detection can remove non-effective nodes and data, and save the energy consumption of the whole network. The improved algorithm extracts features from normal and abnormal behaviour samples by using unsupervised learning and overcomes the problem of unknown or less prior samples. Compared with the commonly used machine learning mechanisms, the proposed ICDBN_IDM achieves high intrusion detection accuracy, reduces the ratio of the false alarm while saving the energy consumption of sensor nodes.
    關聯: International Journal of Ad Hoc and Ubiquitous Computing 36(1), p.20-31
    DOI: 10.1504/IJAHUC.2021.112980
    顯示於類別:[資訊工程學系暨研究所] 期刊論文

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