Εμφάνιση απλής εγγραφής

dc.creatorDemertzis K., Iliadis L., Kikiras P., Tziritas N.en
dc.date.accessioned2023-01-31T07:53:23Z
dc.date.available2023-01-31T07:53:23Z
dc.date.issued2019
dc.identifier10.1007/978-3-030-19823-7_2
dc.identifier.isbn9783030198220
dc.identifier.issn18684238
dc.identifier.urihttp://hdl.handle.net/11615/73202
dc.description.abstractAccording to the Greek mythology, Typhon was a gigantic monster with one hundred dragon heads, bigger than all mountains. His open hands were extending from East to West, his head could reach the sky and flames were coming out of his mouth. His body below the waste consisted of curled snakes. This research effort introduces the “Cyber-Typhon” (CYTY) an Online Multi-Task Anomaly Detection Framework. It aims to fully upgrade old passive infrastructure through an intelligent mechanism, using advanced Computational Intelligence (COIN) algorithms. More specifically, it proposes an intelligent Multi-Task Learning framework, which combines On-Line Sequential Extreme Learning Machines (OS-ELM) and Restricted Boltzmann Machines (RBMs) in order to control data flows. The final target of this model is the intelligent classification of Critical Infrastructures’ network flow, resulting in Anomaly Detection due to Advanced Persistent Threat (APT) attacks. © 2019, IFIP International Federation for Information Processing.en
dc.language.isoenen
dc.sourceIFIP Advances in Information and Communication Technologyen
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85065902707&doi=10.1007%2f978-3-030-19823-7_2&partnerID=40&md5=a038f6411580a687cba0963b4a78cbb5
dc.subjectArtificial intelligenceen
dc.subjectCritical infrastructuresen
dc.subjectLearning systemsen
dc.subjectPublic worksen
dc.subjectContent inspectionsen
dc.subjectCritical infrastructure protectionen
dc.subjectMultitask learningen
dc.subjectOnline learningen
dc.subjectRestricted boltzmann machineen
dc.subjectAnomaly detectionen
dc.subjectSpringer New York LLCen
dc.titleCyber-Typhon: An Online Multi-task Anomaly Detection Frameworken
dc.typeconferenceItemen


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