Hossain, Muhammad IqbalFerdous, RaiyanYeahia, Mamsad IbnRafeed, Munib FahmidMajid, Nafiul2025-05-122025-05-1220242024-12ID 21101127ID 21101163ID 24341128ID 21101169http://hdl.handle.net/10361/25877Cataloged from PDF version of thesis.Includes bibliographical references (pages 59-62).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.Operational Security (OPSEC) is a risk and security management process and approach that first classifies information. There are five steps in OPSEC and the most important step is to identify and analyze potential threats and vulnerabilities. Analyzing threats and vulnerabilities can be done using manual penetration and VAPT where testers simulate real-world attacks the system may face in the future. Moreover, implementing improved machine learning algorithms can detect threats more robustly. By incorporating artificial intelligence with OPSEC, we can mitigate most of these drawbacks. This research identifies four threats; DDoS, Data Breach, Malware, and Phishing and detects them with particular ML/AI models and improves the performance of those models by optimizing. Moreover, this research implemented Explainable-AI (SHAP) in order to easily explain and interpret the models.68 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Operational securityOPSECVAPTThreat modelingVulnerabilityData breachCyber threatsPenetration testingDDoSEx-AIComputer networks--Security measures.Computational intelligence.Data protection.Thesis