Attacking Slicing Network via Side-channel Reinforcement Learning Attack

• URL: http://arxiv.org/abs/2409.11258v1
• Date: Tue, 17 Sep 2024 15:07:05 GMT
• Title: Attacking Slicing Network via Side-channel Reinforcement Learning Attack
• Authors: Wei Shao, Chandra Thapa, Rayne Holland, Sarah Ali Siddiqui, Seyit Camtepe,
• Abstract summary: We introduce a reinforcement learning-based side-channel cache attack framework specifically designed for network slicing environments. Our framework dynamically identifies and exploit cache locations storing sensitive information, such as authentication keys and user registration data. Experimental results showcase the superiority of our approach, achieving a success rate of approximately 95% to 98%.
• Score: 9.428116807615407
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Network slicing in 5G and the future 6G networks will enable the creation of multiple virtualized networks on a shared physical infrastructure. This innovative approach enables the provision of tailored networks to accommodate specific business types or industry users, thus delivering more customized and efficient services. However, the shared memory and cache in network slicing introduce security vulnerabilities that have yet to be fully addressed. In this paper, we introduce a reinforcement learning-based side-channel cache attack framework specifically designed for network slicing environments. Unlike traditional cache attack methods, our framework leverages reinforcement learning to dynamically identify and exploit cache locations storing sensitive information, such as authentication keys and user registration data. We assume that one slice network is compromised and demonstrate how the attacker can induce another shared slice to send registration requests, thereby estimating the cache locations of critical data. By formulating the cache timing channel attack as a reinforcement learning-driven guessing game between the attack slice and the victim slice, our model efficiently explores possible actions to pinpoint memory blocks containing sensitive information. Experimental results showcase the superiority of our approach, achieving a success rate of approximately 95\% to 98\% in accurately identifying the storage locations of sensitive data. This high level of accuracy underscores the potential risks in shared network slicing environments and highlights the need for robust security measures to safeguard against such advanced side-channel attacks.

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