{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/134224"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/134224","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Adaptive Reinforcement Learning-Based Fuzzer for 5G RRC Security Evaluation","abstract":"This thesis presents the development of an adaptive fuzzing framework leveraging the UER- ANSIM simulator to assess the security and resilience of 5G networks. The proposed approach specifically targets potential vulnerabilities in message exchange sequences, authentication procedures, and resource allocation mechanisms. It employs Q-learning to optimize fuzzing strategies across multiple protocol stack layers. The framework evaluates the impact of malicious or malformed inputs on a gNB's stability and performance by emulating multiple user devices and systematically altering key parameters in control-plane messages. A major focus of the study is the simulation of large-scale Distributed Denial-of-Service (DDoS) attacks, wherein numerous simulated UEs generate high volumes of fuzzed signaling traffic to stress the system and observe degradation. The reinforcement learning agent dynamically adjusts its attack patterns based on network feedback, tuning the injection of messages to maximize resource exhaustion and identify critical failure points. The effectiveness of the fuzzing campaign is measured through resource utilization metrics, such as CPU load and Thread count collected during experimentation. By combining adversarial testing with intelligent fuzzing techniques, the research provides important insights into the security posture of 5G infrastructure under adversarial conditions. The findings underscore the need for stronger defenses against protocol-level attacks to bolster the resilience of next-generation wireless communication systems.","abstract_html":"This thesis presents the development of an adaptive fuzzing framework leveraging the UER- ANSIM simulator to assess the security and resilience of 5G networks. The proposed approach specifically targets potential vulnerabilities in message exchange sequences, authentication procedures, and resource allocation mechanisms. It employs Q-learning to optimize fuzzing strategies across multiple protocol stack layers. The framework evaluates the impact of malicious or malformed inputs on a gNB's stability and performance by emulating multiple user devices and systematically altering key parameters in control-plane messages. A major focus of the study is the simulation of large-scale Distributed Denial-of-Service (DDoS) attacks, wherein numerous simulated UEs generate high volumes of fuzzed signaling traffic to stress the system and observe degradation. The reinforcement learning agent dynamically adjusts its attack patterns based on network feedback, tuning the injection of messages to maximize resource exhaustion and identify critical failure points. The effectiveness of the fuzzing campaign is measured through resource utilization metrics, such as CPU load and Thread count collected during experimentation. By combining adversarial testing with intelligent fuzzing techniques, the research provides important insights into the security posture of 5G infrastructure under adversarial conditions. 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The effectiveness of the fuzzing campaign is measured through resource utilization metrics, such as CPU load and Thread count collected during experimentation. By combining adversarial testing with intelligent fuzzing techniques, the research provides important insights into the security posture of 5G infrastructure under adversarial conditions. The findings underscore the need for stronger defenses against protocol-level attacks to bolster the resilience of next-generation wireless communication systems."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["As 5G networks become foundational to modern communications, protecting them from cyberattacks is more important than ever. This research uses a method called fuzzing – an automated testing approach that simulates potential cyberattacks – to test how robust 5G networks are against such threats. By generating and modifying different kinds of network messages, the system actively searches for weaknesses that a real attacker might exploit. One major part of this work is running simulated Distributed Denial-of-Service (DDoS) attacks, where a flood of user requests is sent to see how the network copes under extreme stress. The approach also uses reinforcement learning so that the attack patterns can change on the fly based on how the network responds, which helps uncover vulnerabilities more effectively. Watching how the network reacts to these tests provides valuable insight into possible security gaps and ways to strengthen 5G defenses. Overall, this study highlights the importance of being proactive with 5G security. 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The effectiveness of the fuzzing campaign is measured through resource utilization metrics, such as CPU load and Thread count collected during experimentation. By combining adversarial testing with intelligent fuzzing techniques, the research provides important insights into the security posture of 5G infrastructure under adversarial conditions. The findings underscore the need for stronger defenses against protocol-level attacks to bolster the resilience of next-generation wireless communication systems."],"dc:description.abstractgeneral":["As 5G networks become foundational to modern communications, protecting them from cyberattacks is more important than ever. This research uses a method called fuzzing – an automated testing approach that simulates potential cyberattacks – to test how robust 5G networks are against such threats. By generating and modifying different kinds of network messages, the system actively searches for weaknesses that a real attacker might exploit. One major part of this work is running simulated Distributed Denial-of-Service (DDoS) attacks, where a flood of user requests is sent to see how the network copes under extreme stress. The approach also uses reinforcement learning so that the attack patterns can change on the fly based on how the network responds, which helps uncover vulnerabilities more effectively. Watching how the network reacts to these tests provides valuable insight into possible security gaps and ways to strengthen 5G defenses. Overall, this study highlights the importance of being proactive with 5G security. 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