Title: An intrusion detection model using improved convolutional deep belief networks for wireless sensor networks

Authors: Weimin Wen; Cuijuan Shang; Zaixiu Dong; Huan-Chao Keh; Diptendu Sinha Roy

Addresses: School of Computer and Information Engineering, Chuzhou University, Anhui, 239000, China ' Department of Computer Science and Information Engineering, Tamkang University, New Taipei City, 23741, Taiwan ' School of Computer and Information Engineering, Chuzhou University, Anhui, 239000, China ' Department of Computer Science and Information Engineering, Tamkang University, New Taipei City, 23741, Taiwan ' Department of Computer Science and Engineering, National Institute of Technology Meghalaya, Shillong, 793003, Meghalaya, India

Abstract: 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.

Keywords: intrusion detection; improved convolutional deep belief networks; redundancy detection; deeply compressed algorithm; wireless sensor networks; WSNs.

DOI: 10.1504/IJAHUC.2021.112980

International Journal of Ad Hoc and Ubiquitous Computing, 2021 Vol.36 No.1, pp.20 - 31

Received: 23 Apr 2020
Accepted: 27 Jul 2020
Published online: 12 Feb 2021 *

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