FDA3 : Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applications
This addresses security challenges for cloud-based IIoT deployments by enabling scalable defense against varied attacks, though it is incremental as it builds on federated learning concepts.
The paper tackles the problem of defending against diverse adversarial attacks in Industrial IoT applications by proposing FDA3, a federated defense approach that aggregates knowledge from multiple sources, resulting in DNNs that resist more malicious attacks and prevent new ones compared to existing methods.
Along with the proliferation of Artificial Intelligence (AI) and Internet of Things (IoT) techniques, various kinds of adversarial attacks are increasingly emerging to fool Deep Neural Networks (DNNs) used by Industrial IoT (IIoT) applications. Due to biased training data or vulnerable underlying models, imperceptible modifications on inputs made by adversarial attacks may result in devastating consequences. Although existing methods are promising in defending such malicious attacks, most of them can only deal with limited existing attack types, which makes the deployment of large-scale IIoT devices a great challenge. To address this problem, we present an effective federated defense approach named FDA3 that can aggregate defense knowledge against adversarial examples from different sources. Inspired by federated learning, our proposed cloud-based architecture enables the sharing of defense capabilities against different attacks among IIoT devices. Comprehensive experimental results show that the generated DNNs by our approach can not only resist more malicious attacks than existing attack-specific adversarial training methods, but also can prevent IIoT applications from new attacks.