How to Attack and Defend NextG Radio Access Network Slicing With Reinforcement Learning
| dc.contributor.author | Shi, Yi | en |
| dc.contributor.author | Sagduyu, Yalin E. | en |
| dc.contributor.author | Erpek, Tugba | en |
| dc.contributor.author | Gursoy, M. Cenk | en |
| dc.date.accessioned | 2023-04-10T17:46:36Z | en |
| dc.date.available | 2023-04-10T17:46:36Z | en |
| dc.date.issued | 2023 | en |
| dc.description.abstract | In this paper, reinforcement learning (RL) for network slicing is considered in next generation (NextG) radio access networks, where the base station (gNodeB) allocates resource blocks (RBs) to the requests of user equipments and aims to maximize the total reward of accepted requests over time. Based on adversarial machine learning, a novel over-the-air attack is introduced to manipulate the RL algorithm and disrupt NextG network slicing. The adversary observes the spectrum and builds its own RL based surrogate model that selects which RBs to jam subject to an energy budget with the objective of maximizing the number of failed requests due to jammed RBs. By jamming the RBs, the adversary reduces the RL algorithm's reward. As this reward is used as the input to update the RL algorithm, the performance does not recover even after the adversary stops jamming. This attack is evaluated in terms of both the recovery time and the (maximum and total) reward loss, and it is shown to be much more effective than benchmark (random and myopic) jamming attacks. Different reactive and proactive defense schemes such as suspending the RL algorithm's update once an attack is detected, introducing randomness to the decision process in RL to mislead the learning process of the adversary, or manipulating the feedback (NACK) mechanism such that the adversary may not obtain reliable information are introduced to show that it is viable to defend NextG network slicing against this attack, in terms of improving the RL algorithm's reward. | en |
| dc.description.notes | This effort is supported in part by the U.S. Army Research Office under contract W911NF-17-C-0090. The content of the information does not necessarily reflect the position or the policy of the U.S. Government, and no official endorsement should be inferred. This effort is also supported in part by the Commonwealth Cyber Initiative, an investment in the advancement of cyber RD, innovation, and workforce development. For more information about CCI, visit www.cyberinitiative.org. | en |
| dc.description.sponsorship | U.S. Army Research Office [W911NF-17-C-0090]; Commonwealth Cyber Initiative, an investment in the advancement of cyber RD, innovation, and workforce development | en |
| dc.description.version | Published version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.doi | https://doi.org/10.1109/OJVT.2022.3229229 | en |
| dc.identifier.eissn | 2644-1330 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/114456 | en |
| dc.identifier.volume | 4 | en |
| dc.language.iso | en | en |
| dc.publisher | IEEE | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject | NextG security | en |
| dc.subject | network slicing | en |
| dc.subject | radio access network | en |
| dc.subject | reinforcement learning | en |
| dc.subject | adversarial machine learning | en |
| dc.subject | jamming | en |
| dc.subject | wireless attack | en |
| dc.subject | defense | en |
| dc.title | How to Attack and Defend NextG Radio Access Network Slicing With Reinforcement Learning | en |
| dc.title.serial | IEEE Open Journal of Vehicular Technology | en |
| dc.type | Article - Refereed | en |
| dc.type.dcmitype | Text | en |
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