An intrusion detection model using improved convolutional deep belief networks for wireless sensor networks
學年 109
學期 1
出版（發表）日期 2021-01-28
作品名稱 An intrusion detection model using improved convolutional deep belief networks for wireless sensor networks
作品名稱（其他語言）
著者 Weimin Wen; Cuijuan Shang; Zaixiu Dong; Huan-Chao Keh; Diptendu Sinha Roy
單位
出版者
著錄名稱、卷期、頁數 International Journal of Ad Hoc and Ubiquitous Computing 36(1), p.20-31
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.
關鍵字 intrusion detection;improved convolutional deep belief networks;redundancy detection;deeply compressed algorithm;wireless sensor networks;WSNs
語言 en_US
ISSN 1743-8225
期刊性質 國外
收錄於 SCI
產學合作
通訊作者 Huan-Chao Keh
審稿制度 否
國別 TWN
公開徵稿
出版型式 ,電子版,紙本
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/120147 )

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