教師資料查詢 | 類別: 期刊論文 | 教師: 葛煥昭KEH HUAN-CHAO (瀏覽個人網頁)

標題: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
語言英文(美國)
ISSN1743-8225
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
國別中華民國
公開徵稿
出版型式,電子版,紙本
相關連結
SDGs
  • 優質教育,產業創新與基礎設施
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