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

Kwangjo KIM  

IEICE TRANSACTIONS on Information and Systems   Vol.E103-D   No.7   pp.1433-1447
Publication Date: 2020/07/01
Publicized: 2020/03/31
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2019ICI0001
Type of Manuscript: INVITED PAPER (Special Section on Information and Communication System Security)
intrusion detection system,  deep learning,  feature learning,  anomaly detection,  deep-feature extraction and selection,  

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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.