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Advanced Persistent Threat Detection and Mitigation Using Machine Learning Model

Intelligent Automation & Soft Computing 2023年第6期36卷 3691-3707页

作者： U.Sakthivelu C.N.S.Vinoth Kumar Department of Networking and Communications College of Engineering and Technology(CET)SRM Institute of Science and TechnologyKattankulathurChennai603203India

The detection of cyber threats has recently been a crucial research domain as the internet and data drive people’s *** cyber-attacks lead to the compromise of data *** proposed system offers complete data protection from Advanced Persistent Threat(APT)attacks with attack detection and defence *** modified lateral movement detection algorithm detects the APT attacks,while the defence is achieved by the dynamic deception system that makes use of the belief update *** termination,every cyber-attack undergoes multiple stages,with the most prominent stage being Lateral Movement(LM).The LM uses a Remote Desktop protocol(RDP)technique to authenticate the unauthorised host leaving footprints on the network and host *** anomaly-based approach leveraging the RDP event logs on Windows is used for detecting the evidence of *** extracting various feature sets from the logs,the RDP sessions are classified using machine-learning techniques with high recall and *** is found that the AdaBoost classifier offers better accuracy,precision,F1 score and recall recording 99.9%,99.9%,0.99 and 0.98%.Further,a dynamic deception process is used as a defence mechanism to mitigateAPTattacks.A hybrid encryption communication,dynamic(Internet Protocol)IP address generation,timing selection and policy allocation are established based on mathematical models.A belief update algorithm controls the defender’s *** performance of the proposed system is compared with the state-of-the-art models.

关键词： Advanced persistent threats lateral movement detection dynamic deception remote desktop protocol Internet Protocol attack detection

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