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

Kwangjo KIM

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Summary :

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.

Publication
IEICE TRANSACTIONS on Information 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
Special Section INVITED PAPER (Special Section on Information and Communication System Security)
Category

Authors

Kwangjo KIM
  Korea Advanced Institute of Science and Technology (KAIST)

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