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

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dc.contributor.authorKim, Kwangjoko
dc.date.accessioned2020-07-24T04:55:03Z-
dc.date.available2020-07-24T04:55:03Z-
dc.date.created2020-07-20-
dc.date.created2020-07-20-
dc.date.created2020-07-20-
dc.date.issued2020-07-
dc.identifier.citationIEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, v.E103D, no.7, pp.1433 - 1447-
dc.identifier.issn1745-1361-
dc.identifier.urihttp://hdl.handle.net/10203/275624-
dc.description.abstractDeep 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.languageEnglish-
dc.publisherIEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG-
dc.titleIntrusion Detection System Using Deep Learning and Its Application to Wi-Fi Network-
dc.typeArticle-
dc.identifier.wosid000545550500002-
dc.identifier.scopusid2-s2.0-85089367294-
dc.type.rimsART-
dc.citation.volumeE103D-
dc.citation.issue7-
dc.citation.beginningpage1433-
dc.citation.endingpage1447-
dc.citation.publicationnameIEICE TRANSACTIONS ON INFORMATION AND SYSTEMS-
dc.identifier.doi10.1587/transinf.2019ICI0001-
dc.contributor.localauthorKim, Kwangjo-
dc.description.isOpenAccessY-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorintrusion detection system-
dc.subject.keywordAuthordeep learning-
dc.subject.keywordAuthorfeature learning-
dc.subject.keywordAuthoranomaly detection-
dc.subject.keywordAuthordeep-feature extraction and selection-
dc.subject.keywordPlusFEATURE-SELECTION-
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