Publication:
A Review on Attack Graph Analysis for IoT Vulnerability Assessment: Challenges, Open Issues, and Future Directions

Simple item page
dc.citedby12
dc.contributor.authorAlmazrouei O.S.M.B.H.en_US
dc.contributor.authorMagalingam P.en_US
dc.contributor.authorHasan M.K.en_US
dc.contributor.authorShanmugam M.en_US
dc.contributor.authorid57984794500en_US
dc.contributor.authorid35302809600en_US
dc.contributor.authorid55057479600en_US
dc.contributor.authorid36195134500en_US
dc.date.accessioned2024-10-14T03:21:19Z
dc.date.available2024-10-14T03:21:19Z
dc.date.issued2023
dc.description.abstractVulnerability assessment in industrial IoT networks is critical due to the evolving nature of the domain and the increasing complexity of security threats. This study aims to address the existing gaps in the literature by conducting a comprehensive survey on the use of attack graphs for vulnerability assessment in IoT networks. Attack graphs serve as a valuable cybersecurity tool for modeling and analyzing potential attack scenarios on systems, networks, or applications. The survey covers the research conducted between 2016 and 2021(34 peer-reviewed journal articles and 28 conference papers), identifying and categorizing the main methodologies and technologies employed in generating and analyzing attack graphs. In this review, core modeling techniques for IoT vulnerability assessment are highlighted, such as Markov Decision Processes (MDP), Feature Pyramid Networks (FPN), K-means clustering, and logistic regression models, along with other techniques involving genetic algorithms like fast-forward (FF), contingent fast-forwards (CFF), advanced reinforcement-learning algorithms, and HARMs models. The evaluation of the performance of these attack graph models using IoT networks or devices as case studies is also emphasized. This survey provides valuable insights into the state-of-the-art attack graph techniques for IoT network vulnerability assessment, identifying various applications, performances, research opportunities, and challenges. As a reference source, it serves to inform academicians and practitioners interested in leveraging attack graphs for IoT network vulnerability assessment and guides future research directions in this area. � 2013 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ACCESS.2023.3272053
dc.identifier.epage44376
dc.identifier.scopus2-s2.0-85159673104
dc.identifier.spage44350
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85159673104&doi=10.1109%2fACCESS.2023.3272053&partnerID=40&md5=88ae87caf6da41d99adb6ba743746d5d
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34638
dc.identifier.volume11
dc.pagecount26
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleIEEE Access
dc.subjectAttack graph
dc.subjectnetwork vulnerabilities
dc.subjectthe Internet of Things
dc.subjectvulnerability assessment
dc.subjectClustering algorithms
dc.subjectCybersecurity
dc.subjectGenetic algorithms
dc.subjectGraphic methods
dc.subjectInternet of things
dc.subjectLearning algorithms
dc.subjectMarkov processes
dc.subjectNetwork security
dc.subjectRegression analysis
dc.subjectReinforcement learning
dc.subjectAttack graph
dc.subjectAttacks scenarios
dc.subjectCyber security
dc.subjectFast forward
dc.subjectGraph analysis
dc.subjectNetwork vulnerability
dc.subjectPotential attack
dc.subjectSecurity threats
dc.subjectSystematic
dc.subjectVulnerability assessments
dc.subjectWireless sensor networks
dc.titleA Review on Attack Graph Analysis for IoT Vulnerability Assessment: Challenges, Open Issues, and Future Directionsen_US
dc.typeReviewen_US
dspace.entity.typePublication
Files
Collections
SCOPUS