期刊
INTERNATIONAL JOURNAL OF AD HOC AND UBIQUITOUS COMPUTING
卷 36, 期 1, 页码 20-31出版社
INDERSCIENCE ENTERPRISES LTD
DOI: 10.1504/IJAHUC.2021.112980
关键词
intrusion detection; improved convolutional deep belief networks; redundancy detection; deeply compressed algorithm; wireless sensor networks; WSNs
资金
- Natural Science Foundation of Anhui Higher Education [KJ2019ZD44, KJ2019A0648, KJ2018B01]
- Science and Technology Major Project of Anhui Province [201903a06020026]
- Research Center for Intelligent Perception and Health Endowment Engineering Technology of Department of Education Anhui Province
The study introduces an intrusion detection model based on convolutional deep belief networks, which effectively addresses issues of redundancy and energy consumption in intrusion detection. By utilizing unsupervised learning for feature extraction, the model enhances intrusion detection accuracy and reduces false alarm rates.
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.
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