AI-based Traffic Analysis Attack Mechanism for IoT Systems

With the massive growth of wireless service comes the concern about location privacy. Even with the use of pseudonyms and anonymous communication techniques, the threat of traffic analysis continues to be worrisome. Particularly in the context of Internet of Things (IoT) applications, data packets often flow towards a certain user node or a Base Station (BS), which makes such a recipient a sink in the network and elevates its susceptibility to cyberattacks. Existing countermeasures assume that the attacker will employ one of the contemporary analysis engines, such as Evidence theory and transmission rate, as the underlying data correlation mechanism. This paper presents a novel traffic analysis model that is based on Convolutional Neural Networks (CNN). We demonstrate that such a new attack mechanism is so powerful that it could overcome conventional countermeasures and hence warrants extensive investigation to devise protective measures.