Reading Lips: An Analytical Framework for Adversarial Passive Detection of Wireless Traffic in IoT Ecosystems

Eavesdropping by passive adversaries in wireless communications represents a significant threat, particularly within Internet of Things (IoT) environments, due to the limited security capabilities of such devices and the broadcast nature of wireless communication. An adversary can exploit the broadcast nature of wireless networks to gather metadata such as timing, duration, and frequency of transmissions to possibly infer sensitive information and operational details. Hence, monitoring and eavesdropping threats pose aggravated risks to 5G/6G use-cases driven by massive machine-type communication (e.g., IoT devices). However, there is a gap in our fundamental understanding of how these attacks are developed and conducted, which impacts our ability to develop effective detection and mitigation tools. This article introduces a novel analytical framework to formulate eavesdropping attacks in wireless IoT ecosystems. Specifically, we focus on "adversarial traffic detection attacks" and explore their mechanics, develop an analytical framework for optimal attack strategies, and demonstrate how adversaries might employ algorithmic techniques to enhance their attack effectiveness. Numerical results demonstrate the effectiveness of the proposed adversarial eavesdropping strategy. Finally, we propose cybersecurity countermeasures aimed at reducing the risk of such attacks to IoT systems, making our contribution both timely and relevant.