DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Kwangjo | ko |
dc.date.accessioned | 2020-07-24T04:55:03Z | - |
dc.date.available | 2020-07-24T04:55:03Z | - |
dc.date.created | 2020-07-20 | - |
dc.date.created | 2020-07-20 | - |
dc.date.created | 2020-07-20 | - |
dc.date.issued | 2020-07 | - |
dc.identifier.citation | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, v.E103D, no.7, pp.1433 - 1447 | - |
dc.identifier.issn | 1745-1361 | - |
dc.identifier.uri | http://hdl.handle.net/10203/275624 | - |
dc.description.abstract | 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. | - |
dc.language | English | - |
dc.publisher | IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG | - |
dc.title | Intrusion Detection System Using Deep Learning and Its Application to Wi-Fi Network | - |
dc.type | Article | - |
dc.identifier.wosid | 000545550500002 | - |
dc.identifier.scopusid | 2-s2.0-85089367294 | - |
dc.type.rims | ART | - |
dc.citation.volume | E103D | - |
dc.citation.issue | 7 | - |
dc.citation.beginningpage | 1433 | - |
dc.citation.endingpage | 1447 | - |
dc.citation.publicationname | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS | - |
dc.identifier.doi | 10.1587/transinf.2019ICI0001 | - |
dc.contributor.localauthor | Kim, Kwangjo | - |
dc.description.isOpenAccess | Y | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | intrusion detection system | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | feature learning | - |
dc.subject.keywordAuthor | anomaly detection | - |
dc.subject.keywordAuthor | deep-feature extraction and selection | - |
dc.subject.keywordPlus | FEATURE-SELECTION | - |
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