Matches in SemOpenAlex for { <https://semopenalex.org/work/W4308607121> ?p ?o ?g. }
Showing items 1 to 62 of
62
with 100 items per page.
- W4308607121 abstract "Digitization and remote connectivity have enlarged the attack surface and made cyber systems more vulnerable. As attackers become increasingly sophisticated and resourceful, mere reliance on traditional cyber protection, such as intrusion detection, firewalls, and encryption, is insufficient to secure the cyber systems. Cyber resilience provides a new security paradigm that complements inadequate protection with resilience mechanisms. A Cyber-Resilient Mechanism (CRM) adapts to the known or zero-day threats and uncertainties in real-time and strategically responds to them to maintain critical functions of the cyber systems in the event of successful attacks. Feedback architectures play a pivotal role in enabling the online sensing, reasoning, and actuation process of the CRM. Reinforcement Learning (RL) is an essential tool that epitomizes the feedback architectures for cyber resilience. It allows the CRM to provide sequential responses to attacks with limited or without prior knowledge of the environment and the attacker. In this work, we review the literature on RL for cyber resilience and discuss cyber resilience against three major types of vulnerabilities, i.e., posture-related, information-related, and human-related vulnerabilities. We introduce three application domains of CRMs: moving target defense, defensive cyber deception, and assistive human security technologies. The RL algorithms also have vulnerabilities themselves. We explain the three vulnerabilities of RL and present attack models where the attacker targets the information exchanged between the environment and the agent: the rewards, the state observations, and the action commands. We show that the attacker can trick the RL agent into learning a nefarious policy with minimum attacking effort. Lastly, we discuss the future challenges of RL for cyber security and resilience and emerging applications of RL-based CRMs." @default.
- W4308607121 created "2022-11-13" @default.
- W4308607121 creator A5029546270 @default.
- W4308607121 creator A5060454132 @default.
- W4308607121 creator A5081500464 @default.
- W4308607121 date "2021-07-01" @default.
- W4308607121 modified "2023-09-27" @default.
- W4308607121 title "Reinforcement Learning for Feedback-Enabled Cyber Resilience" @default.
- W4308607121 doi "https://doi.org/10.48550/arxiv.2107.00783" @default.
- W4308607121 hasPublicationYear "2021" @default.
- W4308607121 type Work @default.
- W4308607121 citedByCount "0" @default.
- W4308607121 crossrefType "posted-content" @default.
- W4308607121 hasAuthorship W4308607121A5029546270 @default.
- W4308607121 hasAuthorship W4308607121A5060454132 @default.
- W4308607121 hasAuthorship W4308607121A5081500464 @default.
- W4308607121 hasBestOaLocation W43086071211 @default.
- W4308607121 hasConcept C111919701 @default.
- W4308607121 hasConcept C121332964 @default.
- W4308607121 hasConcept C154945302 @default.
- W4308607121 hasConcept C15744967 @default.
- W4308607121 hasConcept C201307755 @default.
- W4308607121 hasConcept C2779267917 @default.
- W4308607121 hasConcept C2779585090 @default.
- W4308607121 hasConcept C35525427 @default.
- W4308607121 hasConcept C38652104 @default.
- W4308607121 hasConcept C41008148 @default.
- W4308607121 hasConcept C77805123 @default.
- W4308607121 hasConcept C97355855 @default.
- W4308607121 hasConcept C97541855 @default.
- W4308607121 hasConcept C98045186 @default.
- W4308607121 hasConceptScore W4308607121C111919701 @default.
- W4308607121 hasConceptScore W4308607121C121332964 @default.
- W4308607121 hasConceptScore W4308607121C154945302 @default.
- W4308607121 hasConceptScore W4308607121C15744967 @default.
- W4308607121 hasConceptScore W4308607121C201307755 @default.
- W4308607121 hasConceptScore W4308607121C2779267917 @default.
- W4308607121 hasConceptScore W4308607121C2779585090 @default.
- W4308607121 hasConceptScore W4308607121C35525427 @default.
- W4308607121 hasConceptScore W4308607121C38652104 @default.
- W4308607121 hasConceptScore W4308607121C41008148 @default.
- W4308607121 hasConceptScore W4308607121C77805123 @default.
- W4308607121 hasConceptScore W4308607121C97355855 @default.
- W4308607121 hasConceptScore W4308607121C97541855 @default.
- W4308607121 hasConceptScore W4308607121C98045186 @default.
- W4308607121 hasLocation W43086071211 @default.
- W4308607121 hasLocation W43086071212 @default.
- W4308607121 hasOpenAccess W4308607121 @default.
- W4308607121 hasPrimaryLocation W43086071211 @default.
- W4308607121 hasRelatedWork W2030140501 @default.
- W4308607121 hasRelatedWork W2406432589 @default.
- W4308607121 hasRelatedWork W2485875719 @default.
- W4308607121 hasRelatedWork W2761984581 @default.
- W4308607121 hasRelatedWork W2893477953 @default.
- W4308607121 hasRelatedWork W2903029239 @default.
- W4308607121 hasRelatedWork W2909062875 @default.
- W4308607121 hasRelatedWork W2914048830 @default.
- W4308607121 hasRelatedWork W4366251006 @default.
- W4308607121 hasRelatedWork W4379116325 @default.
- W4308607121 isParatext "false" @default.
- W4308607121 isRetracted "false" @default.
- W4308607121 workType "article" @default.