Generative AI for Internet of Things Security: Challenges and Opportunities

As Generative AI (GenAI) continues to gain prominence and utility across various sectors, their integration into the realm of Internet of Things (IoT) security evolves rapidly. This work delves into an examination of the state-of-the-art literature and practical applications on how GenAI could improve and be applied in the security landscape of IoT. Our investigation aims to map the current state of GenAI implementation within IoT security, exploring their potential to fortify security measures further. Through the compilation, synthesis, and analysis of the latest advancements in GenAI technologies applied to IoT, this paper not only introduces fresh insights into the field, but also lays the groundwork for future research directions. It explains the prevailing challenges within IoT security, discusses the effectiveness of GenAI in addressing these issues, and identifies significant research gaps through MITRE Mitigations. Accompanied with three case studies, we provide a comprehensive overview of the progress and future prospects of GenAI applications in IoT security. This study serves as a foundational resource to improve IoT security through the innovative application of GenAI, thus contributing to the broader discourse on IoT security and technology integration.

Practical and General Backdoor Attacks against Vertical Federated Learning

Federated learning (FL), which aims to facilitate data collaboration across multiple organizations without exposing data privacy, encounters potential security risks. One serious threat is backdoor attacks, where an attacker injects a specific trigger into the training dataset to manipulate the model's prediction. Most existing FL backdoor attacks are based on horizontal federated learning (HFL), where the data owned by different parties have the same features. However, compared to HFL, backdoor attacks on vertical federated learning (VFL), where each party only holds a disjoint subset of features and the labels are only owned by one party, are rarely studied. The main challenge of this attack is to allow an attacker without access to the data labels, to perform an effective attack. To this end, we propose BadVFL, a novel and practical approach to inject backdoor triggers into victim models without label information. BadVFL mainly consists of two key steps. First, to address the challenge of attackers having no knowledge of labels, we introduce a SDD module that can trace data categories based on gradients. Second, we propose a SDP module that can improve the attack's effectiveness by enhancing the decision dependency between the trigger and attack target. Extensive experiments show that BadVFL supports diverse datasets and models, and achieves over 93% attack success rate with only 1% poisoning rate.