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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2308.05978 |
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| _version_ | 1866914960386293760 |
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| author | Feng, Chao Celdran, Alberto Huertas Sanchez, Pedro Miguel Sanchez Kreischer, Jan von der Assen, Jan Bovet, Gerome Perez, Gregorio Martinez Stiller, Burkhard |
| author_facet | Feng, Chao Celdran, Alberto Huertas Sanchez, Pedro Miguel Sanchez Kreischer, Jan von der Assen, Jan Bovet, Gerome Perez, Gregorio Martinez Stiller, Burkhard |
| contents | Recent research has shown that the integration of Reinforcement Learning (RL) with Moving Target Defense (MTD) can enhance cybersecurity in Internet-of-Things (IoT) devices. Nevertheless, the practicality of existing work is hindered by data privacy concerns associated with centralized data processing in RL, and the unsatisfactory time needed to learn right MTD techniques that are effective against a rising number of heterogeneous zero-day attacks. Thus, this work presents CyberForce, a framework that combines Federated and Reinforcement Learning (FRL) to collaboratively and privately learn suitable MTD techniques for mitigating zero-day attacks. CyberForce integrates device fingerprinting and anomaly detection to reward or penalize MTD mechanisms chosen by an FRL-based agent. The framework has been deployed and evaluated in a scenario consisting of ten physical devices of a real IoT platform affected by heterogeneous malware samples. A pool of experiments has demonstrated that CyberForce learns the MTD technique mitigating each attack faster than existing RL-based centralized approaches. In addition, when various devices are exposed to different attacks, CyberForce benefits from knowledge transfer, leading to enhanced performance and reduced learning time in comparison to recent works. Finally, different aggregation algorithms used during the agent learning process provide CyberForce with notable robustness to malicious attacks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_05978 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation Feng, Chao Celdran, Alberto Huertas Sanchez, Pedro Miguel Sanchez Kreischer, Jan von der Assen, Jan Bovet, Gerome Perez, Gregorio Martinez Stiller, Burkhard Cryptography and Security Artificial Intelligence Recent research has shown that the integration of Reinforcement Learning (RL) with Moving Target Defense (MTD) can enhance cybersecurity in Internet-of-Things (IoT) devices. Nevertheless, the practicality of existing work is hindered by data privacy concerns associated with centralized data processing in RL, and the unsatisfactory time needed to learn right MTD techniques that are effective against a rising number of heterogeneous zero-day attacks. Thus, this work presents CyberForce, a framework that combines Federated and Reinforcement Learning (FRL) to collaboratively and privately learn suitable MTD techniques for mitigating zero-day attacks. CyberForce integrates device fingerprinting and anomaly detection to reward or penalize MTD mechanisms chosen by an FRL-based agent. The framework has been deployed and evaluated in a scenario consisting of ten physical devices of a real IoT platform affected by heterogeneous malware samples. A pool of experiments has demonstrated that CyberForce learns the MTD technique mitigating each attack faster than existing RL-based centralized approaches. In addition, when various devices are exposed to different attacks, CyberForce benefits from knowledge transfer, leading to enhanced performance and reduced learning time in comparison to recent works. Finally, different aggregation algorithms used during the agent learning process provide CyberForce with notable robustness to malicious attacks. |
| title | CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2308.05978 |