[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85617-en":3,"doc-seo-85617-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},85617,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Amplitude Belief Reinforcement Learning for Adaptive Cyber Defense in Partially Observable V2X Networks","Internet of Vehicles (IoV) creates a partially observable, adversarial V2X setting where malicious vehicles probe, attack, and adapt their behavior over time. Traditional intrusion-detection evaluations treat defense as static classification, limiting sequential mitigation against adaptive attackers. The work formulates IoV cyber defense as a partially observable sequential decision problem and proposes QBIRD, an amplitude-belief reinforcement learning framework that maps complex belief amplitudes to intent probabilities, enabling cost-aware PPO mitigation.","Amplitude-Belief Reinforcement Learning for Adaptive Cyber Defense in Partially Observable  \nV2X Networks  \nAnwar Shah, Member, IEEE, Rohan Farooq, Member, IEEE, Sajid Anwer, Tallha Akram, Usman Ghous, Sajid  \nUllah Khan  \narXiv :2606 .07796v2 [ cs .CR] 11 Jul 2026  \nAbstract—The Internet of Vehicles (IoV) creates a partially observable and adversarial V2X communication environment in which malicious vehicles may probe, attack, and evade defensive mechanisms over time. Existing IoV intrusion-detection methods are often evaluated as static classification systems and therefore provide limited support for sequential mitigation under adaptive attacker behavior. This paper formulates IoV cyber defense as a partially observable sequential decision problem and proposes Quantum Belief-Integrated Reinforcement Defense (QBIRD), an amplitude-belief reinforcement learning framework for adaptive V2X defense. Q-BIRD represents uncertainty over hidden attacker intent through a normalized complex-valued belief state and converts amplitudes into intent probabilities through a Born-rule-inspired mapping. The resulting belief features are used by a Proximal Policy Optimization defender to select cost-aware mitigation actions, including monitoring, alerting, throttling, and isolation. Experiments are conducted in a SUMO–OMNeT++/Veins V2X co-simulation environment using IEEE 802.11p/DSRC-based V2V and V2I communication. Across 10 independent random seeds and 80 test episodes per seed, Q-BIRD reduces mean cumulative damage from 36.0±5.5 to 28.0 ± 3.0 compared with PPO using classical Bayesian belief, corresponding to a 22.2% reduction. It also reduces damage variance from 12.0 ± 2.8 to 6.0 ± 1.5, corresponding to a 50.0% reduction. The attack success rate decreases from 0.12 ± 0.03 to 0.05 ± 0.02, while survival probability increases from 0.91±0 .02 to 0.96±0 .02. Communication-level results show that Q-BIRD maintains a packet delivery ratio of 0.94 ± 0.02, latency of 45 ± 6 ms, throughput of 3.60 ± 0.15 Mbps, and service availability of 0.95 ± 0.02. Explainability analysis using SHAP, LIME, and Grad-CAM suggests that belief-related features contribute strongly to mitigation decisions during attacker strategy transitions. These results indicate that amplitude-based belief modeling can improve both cyber-defense stability and V2X communication reliability under partial observability.  \nIndex Terms—Internet of Vehicles, V2X security, reinforcement learning, partial observability, amplitude belief, adaptive cyber defense  \nAnwar Shah is with the Department of Data Science and Artificial Intelligence and Cyberarian Research Lab(email: [anwar.shah@nu.edu.pk](anwar.shah@nu.edu.pk)).  \nRohan Farooq is with Department of Computer Science (e-mail: ro[han.farooq@nu.edu.pk](han.farooq@nu.edu.pk)).  \nSajid Anwer is with Department of Software Engineering, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj(email: [s.anwer@psau.edu.sa](s.anwer@psau.edu.sa)).  \nTallha Akram is with Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj(email: [t.akram@psau.edu.sa](t.akram@psau.edu.sa)).  \nUsman Ghous is with Department of Computer Science(email: us[man.ghous@nu.edu.pk](man.ghous@nu.edu.pk)).  \nSajid Ullah Khan is with Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj(email: [sk.khan@psau.edu.sa](sk.khan@psau.edu.sa)).  \nI. INTRODUCTION  \nThe Internet of Vehicles (IoV) enables large-scale connectivity among vehicles, roadside units, and cloud services to support safety-critical applications such as cooperative driving, traffic management, and autonomous transportation systems [1], [2] . While this connectivity improves efficiency and situational awareness, it also significantly expands the attack surface of vehicular networks, exposing IoV systems to cyber threats, inc","cbCairqUGHrXe4e2","https://ap.wps.com/l/cbCairqUGHrXe4e2","pdf",18869176,3,1,16,"English","en",105,"# Introduction\n## Cyber threats in IoV and V2X\n## Limits of static intrusion detection\n## Reinforcement learning and game-theoretic defense\n## Partial observability and belief modeling challenges","[{\"question\":\"What problem does the paper address in IoV cyber defense?\",\"answer\":\"It addresses sequential cyber defense for V2X networks under partial observability, where attacker intent is hidden and adversaries adapt their behavior over time.\"},{\"question\":\"How does QBIRD represent attacker uncertainty and intent?\",\"answer\":\"QBIRD uses a normalized complex-valued belief state to encode uncertainty over hidden attacker intent, then converts amplitudes into intent probabilities with a Born-rule-inspired mapping.\"},{\"question\":\"What mitigation actions does the proposed defender choose?\",\"answer\":\"The PPO-based defender selects cost-aware mitigation actions such as monitoring, alerting, throttling, and 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problem does the paper address in IoV cyber defense?","Question",{"text":75,"@type":76},"It addresses sequential cyber defense for V2X networks under partial observability, where attacker intent is hidden and adversaries adapt their behavior over time.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does QBIRD represent attacker uncertainty and intent?",{"text":80,"@type":76},"QBIRD uses a normalized complex-valued belief state to encode uncertainty over hidden attacker intent, then converts amplitudes into intent probabilities with a Born-rule-inspired mapping.",{"name":82,"@type":73,"acceptedAnswer":83},"What mitigation actions does the proposed defender choose?",{"text":84,"@type":76},"The PPO-based defender selects cost-aware mitigation actions such as monitoring, alerting, throttling, and 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