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CNN-based anomaly detection for packet payloads of industrial control systemoa mark
  • Song, Joo Yeop ;
  • Paul, Rajib ;
  • Yun, Jeong Han ;
  • Kim, Hyoung Chun ;
  • Choi, Young June
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dc.contributor.authorSong, Joo Yeop-
dc.contributor.authorPaul, Rajib-
dc.contributor.authorYun, Jeong Han-
dc.contributor.authorKim, Hyoung Chun-
dc.contributor.authorChoi, Young June (researcherId=7406117220; isni=0000000405323933; orcid=https://orcid.org/0000-0003-2014-6587)-
dc.date.issued2021-01-01-
dc.identifier.issn1748-1287-
dc.identifier.urihttps://aurora.ajou.ac.kr/handle/2018.oak/32084-
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85107952914&origin=inward-
dc.description.abstractIndustrial control systems (ICSs) are more vulnerable to cyber threats owing to their network connectivity. The intrusion detection system(IDS) has been deployed to detect sophisticated cyber-Attack but the existing IDS uses the packet header information for traffic flow detection. IDS is inefficient to detect packet deformation; therefore, we propose the adoption of packet payload in IDS to respond to a variety of attacks and high performance. Our proposed model detects packet modification and traffic flowby inspecting each packet and sequence of packets. For evaluation, cross verification is conducted to increase the reliability of the statistics.-
dc.description.sponsorshipThis research was supported by Korea Electric Power Corporation [Grant number: 18A-013].-
dc.language.isoeng-
dc.publisherInderscience Publishers-
dc.subject.meshAnomaly detection-
dc.subject.meshConvolutional neural network-
dc.subject.meshIndustrial control systems-
dc.subject.meshIntrusion Detection Systems-
dc.subject.meshIntrusion-Detection-
dc.subject.meshN-gram methods-
dc.subject.meshNetworks security-
dc.subject.meshPacket detection-
dc.subject.meshSequence detection-
dc.subject.meshSingle packet detection-
dc.titleCNN-based anomaly detection for packet payloads of industrial control system-
dc.typeArticle-
dc.citation.endPage49-
dc.citation.number1-
dc.citation.startPage36-
dc.citation.titleInternational Journal of Sensor Networks-
dc.citation.volume36-
dc.identifier.bibliographicCitationInternational Journal of Sensor Networks, Vol.36 No.1, pp.36-49-
dc.identifier.doi10.1504/ijsnet.2021.115440-
dc.identifier.scopusid2-s2.0-85107952914-
dc.identifier.urlhttp://www.inderscience.com/ijsnet-
dc.subject.keywordAnomaly detection-
dc.subject.keywordConvolutional neural network-
dc.subject.keywordIndustrial control system-
dc.subject.keywordIntrusion detection-
dc.subject.keywordn-gram method-
dc.subject.keywordnetwork security-
dc.subject.keywordSequence detection-
dc.subject.keywordSingle packet detection-
dc.type.otherArticle-
dc.identifier.pissn1748-1279-
dc.description.isoatrue-
dc.subject.subareaControl and Systems Engineering-
dc.subject.subareaComputer Science Applications-
dc.subject.subareaComputer Networks and Communications-
dc.subject.subareaElectrical and Electronic Engineering-
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