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Cyber-Typhon: An Online Multi-task Anomaly Detection Framework

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Autore
Demertzis K., Iliadis L., Kikiras P., Tziritas N.
Data
2019
Language
en
DOI
10.1007/978-3-030-19823-7_2
Soggetto
Artificial intelligence
Critical infrastructures
Learning systems
Public works
Content inspections
Critical infrastructure protection
Multitask learning
Online learning
Restricted boltzmann machine
Anomaly detection
Springer New York LLC
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Abstract
According 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.
URI
http://hdl.handle.net/11615/73202
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