EI-MTD:Moving Target Defense for Edge Intelligence against Adversarial Attacks
This addresses the problem of securing edge intelligence against adversarial attacks for applications on resource-constrained devices, representing an incremental improvement in defense mechanisms.
The paper tackles the vulnerability of edge intelligence to adversarial attacks by proposing EI-MTD, a dynamic defense mechanism that uses knowledge distillation and game theory to protect models on resource-limited edge nodes, with experimental results showing effective protection against black-box attacks.
With the boom of edge intelligence, its vulnerability to adversarial attacks becomes an urgent problem. The so-called adversarial example can fool a deep learning model on the edge node to misclassify. Due to the property of transferability, the adversary can easily make a black-box attack using a local substitute model. Nevertheless, the limitation of resource of edge nodes cannot afford a complicated defense mechanism as doing on the cloud data center. To overcome the challenge, we propose a dynamic defense mechanism, namely EI-MTD. It first obtains robust member models with small size through differential knowledge distillation from a complicated teacher model on the cloud data center. Then, a dynamic scheduling policy based on a Bayesian Stackelberg game is applied to the choice of a target model for service. This dynamic defense can prohibit the adversary from selecting an optimal substitute model for black-box attacks. Our experimental result shows that this dynamic scheduling can effectively protect edge intelligence against adversarial attacks under the black-box setting.