Intrusion Detection System Using Deep Learning and Its Application to Wi-Fi Network

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Deep learning is gaining more and more lots of attractions and better performance in implementing the Intrusion Detection System (IDS), especially for feature learning. This paper presents the state-of-the-art advances and challenges in IDS using deep learning models, which have been achieved the big performance enhancements in the field of computer vision, natural language processing, and image/audio processing than the traditional methods. After providing a systematic and methodical description of the latest developments in deep learning from the points of the deployed architectures and techniques, we suggest the pros-and-cons of all the deep learning-based IDS, and discuss the importance of deep learning models as feature learning approach. For this, the author has suggested the concept of the Deep-Feature Extraction and Selection (D-FES). By combining the stacked feature extraction and the weighted feature selection for D-FES, our experiment was verified to get the best performance of detection rate, 99.918% and false alarm rate, 0.012% to detect the impersonation attacks in Wi-Fi network which can be achieved better than the previous publications. Summary and further challenges are suggested as a concluding remark.
Publisher
IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG
Issue Date
2020-07
Language
English
Article Type
Article
Citation

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, v.E103D, no.7, pp.1433 - 1447

ISSN
1745-1361
DOI
10.1587/transinf.2019ICI0001
URI
http://hdl.handle.net/10203/275624
Appears in Collection
CS-Journal Papers(저널논문)
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