Mitigating Evasion Attacks in Fog Computing Resource Provisioning Through Proactive Hardening

2026-03-26

AI Summary

This work addresses the vulnerability of k-means-based resource allocation systems in fog computing to causal evasion attacks, wherein adversaries exploit reverse engineering to compromise model integrity and allocation stability. To counter this threat, the paper introduces adversarial training into fog computing resource scheduling for the first time, embedding an adversarial example augmentation mechanism during the online k-means clustering phase. This approach enables end-to-end defense against both exploratory and causal evasion attacks. Experimental results demonstrate that the proposed method significantly enhances the robustness of the clustering model under adversarial conditions while effectively preserving the stability of virtual machine resource allocation.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Computer Vision: Adversarial Attacks & Robustness
• Multiagent Systems: Adversarial Agents

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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.

Problem

• evasion attacks
• fog computing
• resource provisioning
• model integrity
• k-means clustering

Innovation

• evasion attacks
• adversarial training
• fog computing
• k-means clustering
• proactive hardening