Smart Jamming Attack and Mitigation on Deep Transfer Reinforcement Learning Enabled Resource Allocation for Network Slicing
Network slicing is a pivotal paradigm in wireless networks enabling customized services to users and applications. Yet, intelligent jamming attacks threaten the performance of network slicing. In this paper, we focus on the security aspect of network slicing over a deep transfer reinforcement learni...
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IEEE
2024-01-01
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| Series: | IEEE Transactions on Machine Learning in Communications and Networking |
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| Online Access: | https://ieeexplore.ieee.org/document/10699421/ |
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| author | Shavbo Salehi Hao Zhou Medhat Elsayed Majid Bavand Raimundas Gaigalas Yigit Ozcan Melike Erol-Kantarci |
| author_facet | Shavbo Salehi Hao Zhou Medhat Elsayed Majid Bavand Raimundas Gaigalas Yigit Ozcan Melike Erol-Kantarci |
| author_sort | Shavbo Salehi |
| collection | DOAJ |
| description | Network slicing is a pivotal paradigm in wireless networks enabling customized services to users and applications. Yet, intelligent jamming attacks threaten the performance of network slicing. In this paper, we focus on the security aspect of network slicing over a deep transfer reinforcement learning (DTRL) enabled scenario. We first demonstrate how a deep reinforcement learning (DRL)-enabled jamming attack exposes potential risks. In particular, the attacker can intelligently jam resource blocks (RBs) reserved for slices by monitoring transmission signals and perturbing the assigned resources. Then, we propose a DRL-driven mitigation model to mitigate the intelligent attacker. Specifically, the defense mechanism generates interference on unallocated RBs where another antenna is used for transmitting powerful signals. This causes the jammer to consider these RBs as allocated RBs and generate interference for those instead of the allocated RBs. The analysis revealed that the intelligent DRL-enabled jamming attack caused a significant 50% degradation in network throughput and 60% increase in latency in comparison with the no-attack scenario. However, with the implemented mitigation measures, we observed 80% improvement in network throughput and 70% reduction in latency in comparison to the under-attack scenario. |
| format | Article |
| id | doaj-art-41c58bfeb6d5424abf7c0ec24ea6145f |
| institution | DOAJ |
| issn | 2831-316X |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Machine Learning in Communications and Networking |
| spelling | doaj-art-41c58bfeb6d5424abf7c0ec24ea6145f2025-08-20T02:59:24ZengIEEEIEEE Transactions on Machine Learning in Communications and Networking2831-316X2024-01-0121492150810.1109/TMLCN.2024.347076010699421Smart Jamming Attack and Mitigation on Deep Transfer Reinforcement Learning Enabled Resource Allocation for Network SlicingShavbo Salehi0https://orcid.org/0009-0001-2888-0033Hao Zhou1https://orcid.org/0000-0002-5511-4609Medhat Elsayed2https://orcid.org/0000-0002-1106-6078Majid Bavand3https://orcid.org/0000-0002-8331-0033Raimundas Gaigalas4Yigit Ozcan5Melike Erol-Kantarci6https://orcid.org/0000-0001-6787-8457School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, CanadaSchool of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, CanadaEricsson, Ottawa, ON, CanadaEricsson, Ottawa, ON, CanadaEricsson AB, Stockhom, SwedenEricsson, Ottawa, ON, CanadaSchool of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, CanadaNetwork slicing is a pivotal paradigm in wireless networks enabling customized services to users and applications. Yet, intelligent jamming attacks threaten the performance of network slicing. In this paper, we focus on the security aspect of network slicing over a deep transfer reinforcement learning (DTRL) enabled scenario. We first demonstrate how a deep reinforcement learning (DRL)-enabled jamming attack exposes potential risks. In particular, the attacker can intelligently jam resource blocks (RBs) reserved for slices by monitoring transmission signals and perturbing the assigned resources. Then, we propose a DRL-driven mitigation model to mitigate the intelligent attacker. Specifically, the defense mechanism generates interference on unallocated RBs where another antenna is used for transmitting powerful signals. This causes the jammer to consider these RBs as allocated RBs and generate interference for those instead of the allocated RBs. The analysis revealed that the intelligent DRL-enabled jamming attack caused a significant 50% degradation in network throughput and 60% increase in latency in comparison with the no-attack scenario. However, with the implemented mitigation measures, we observed 80% improvement in network throughput and 70% reduction in latency in comparison to the under-attack scenario.https://ieeexplore.ieee.org/document/10699421/Network slicingdeep transfer reinforcement learningintelligent jamming attackjamming attack mitigation |
| spellingShingle | Shavbo Salehi Hao Zhou Medhat Elsayed Majid Bavand Raimundas Gaigalas Yigit Ozcan Melike Erol-Kantarci Smart Jamming Attack and Mitigation on Deep Transfer Reinforcement Learning Enabled Resource Allocation for Network Slicing IEEE Transactions on Machine Learning in Communications and Networking Network slicing deep transfer reinforcement learning intelligent jamming attack jamming attack mitigation |
| title | Smart Jamming Attack and Mitigation on Deep Transfer Reinforcement Learning Enabled Resource Allocation for Network Slicing |
| title_full | Smart Jamming Attack and Mitigation on Deep Transfer Reinforcement Learning Enabled Resource Allocation for Network Slicing |
| title_fullStr | Smart Jamming Attack and Mitigation on Deep Transfer Reinforcement Learning Enabled Resource Allocation for Network Slicing |
| title_full_unstemmed | Smart Jamming Attack and Mitigation on Deep Transfer Reinforcement Learning Enabled Resource Allocation for Network Slicing |
| title_short | Smart Jamming Attack and Mitigation on Deep Transfer Reinforcement Learning Enabled Resource Allocation for Network Slicing |
| title_sort | smart jamming attack and mitigation on deep transfer reinforcement learning enabled resource allocation for network slicing |
| topic | Network slicing deep transfer reinforcement learning intelligent jamming attack jamming attack mitigation |
| url | https://ieeexplore.ieee.org/document/10699421/ |
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