DIVERGENCE: Deep Reinforcement Learning-Based Adaptive Traffic Inspection and Moving Target Defense Countermeasure Framework

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dc.contributor.authorKim, Sunghwanen
dc.contributor.authorYoon, Seunghyunen
dc.contributor.authorCho, Jin-Heeen
dc.contributor.authorKim, Dong Seongen
dc.contributor.authorMoore, Terrence J.en
dc.contributor.authorFree-Nelson, Fredericaen
dc.contributor.authorLim, Hyuken
dc.date.accessioned2023-04-17T15:05:49Zen
dc.date.available2023-04-17T15:05:49Zen
dc.date.issued2022-12en
dc.description.abstractReinforcement learning (RL) is a promising approach for intelligent agents to protect a given system under highly hostile environments. RL allows the agent to adaptively make sequential defense decisions based on the perceived current state of system security aiming to achieve the maximum defense performance in terms of fast, efficient, and automated detection, threat analysis, and response to the threat. In this paper, we propose a deep reinforcement learning (DRL)-based adaptive traffic inspection and moving target defense countermeasure framework, called 'DIVERGENCE,' for building a secure networked system. The DIVERGENCE provides two main security services: (1) a DRL-based network traffic inspection mechanism to achieve scalable and intensive network traffic visibility for rapid threat detection; and (2) an address shuffling-based moving target defense (MTD) technique to defend against threats as a proactive intrusion prevention mechanism. Through extensive simulations and experiments, we demonstrate that the DIVERGENCE successfully caught malicious traffic flows while significantly reducing the vulnerability of the network through MTD.en
dc.description.notesThis material is based upon work supported by the International Technology Center Pacific (ITC-PAC) under Contract No. FA520920C0022, and the research was partly supported by the Army Research Office under Grant Contract Numbers W91NF-20-2-014 and NSF Grant 2107450.en
dc.description.sponsorshipInternational Technology Center Pacific (ITC-PAC) [FA520920C0022]; Army Research Office [W91NF-20-2-014]; NSF [2107450]en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1109/TNSM.2021.3139928en
dc.identifier.issue4en
dc.identifier.urihttp://hdl.handle.net/10919/114524en
dc.identifier.volume19en
dc.language.isoenen
dc.publisherIEEEen
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectInspectionen
dc.subjectIP networksen
dc.subjectSwitchesen
dc.subjectResource managementen
dc.subjectMonitoringen
dc.subjectUncertaintyen
dc.subjectControl systemsen
dc.subjectTraffic inspectionen
dc.subjectmoving target defenseen
dc.subjectdeep reinforcement learningen
dc.subjectsoftware-defined networkingen
dc.titleDIVERGENCE: Deep Reinforcement Learning-Based Adaptive Traffic Inspection and Moving Target Defense Countermeasure Frameworken
dc.title.serialIEEE Transactions on Network and Service Managementen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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