Mitigating Evasion Attacks in Fog Computing Resource Provisioning Through Proactive Hardening
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866908915570049024 |
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| author | Salmi, Younes Bogucka, Hanna |
| author_facet | Salmi, Younes Bogucka, Hanna |
| contents | This paper investigates the susceptibility to model integrity attacks that overload virtual machines assigned by the k-means algorithm used for resource provisioning in fog networks. The considered k-means algorithm runs two phases iteratively: offline clustering to form clusters of requested workload and online classification of new incoming requests into offline-created clusters. First, we consider an evasion attack against the classifier in the online phase. A threat actor launches an exploratory attack using query-based reverse engineering to discover the Machine Learning (ML) model (the clustering scheme). Then, a passive causative (evasion) attack is triggered in the offline phase. To defend the model, we suggest a proactive method using adversarial training to introduce attack robustness into the classifier. Our results show that our mitigation technique effectively maintains the stability of the resource provisioning system against attacks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_25257 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Mitigating Evasion Attacks in Fog Computing Resource Provisioning Through Proactive Hardening Salmi, Younes Bogucka, Hanna Cryptography and Security Machine Learning This paper investigates the susceptibility to model integrity attacks that overload virtual machines assigned by the k-means algorithm used for resource provisioning in fog networks. The considered k-means algorithm runs two phases iteratively: offline clustering to form clusters of requested workload and online classification of new incoming requests into offline-created clusters. First, we consider an evasion attack against the classifier in the online phase. A threat actor launches an exploratory attack using query-based reverse engineering to discover the Machine Learning (ML) model (the clustering scheme). Then, a passive causative (evasion) attack is triggered in the offline phase. To defend the model, we suggest a proactive method using adversarial training to introduce attack robustness into the classifier. Our results show that our mitigation technique effectively maintains the stability of the resource provisioning system against attacks. |
| title | Mitigating Evasion Attacks in Fog Computing Resource Provisioning Through Proactive Hardening |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2603.25257 |