# Mitigating Evasion Attacks in Fog Computing Resource Provisioning Through Proactive Hardening

arXiv: https://arxiv.org/abs/2603.25257 · HTML: https://mlsanity.com/paper/2603.25257/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 25 references that are in the map.
- First posted on arXiv 26 Mar 2026.

## Builds on (most cited first, 2 of 2)

- [Secure Federated Learning for Cognitive Radio Sensing](https://mlsanity.com/paper/2304.06519/) (2023)
- [Task Allocation for Energy Optimization in Fog Computing Networks with Latency Constraints](https://mlsanity.com/paper/2205.06531/) (2022)

## Abstract (arXiv)

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.