Evolution of IoT Security: The Era of Smart Attacks
Émilie Bout, Valéria Loscrì, Antoine Gallais
Abstract
Internet of Things devices have become the new privileged targets for cyberattackers in recent years. This is in part due to the amount of sensitive data they provide. In parallel, security systems are becoming more and more robust with the help of machine learning (ML) algorithms. However, this advance can also benefit malicious people. In recent years, ML-based smart attacks have emerged and are significantly changing the threat landscape. This article focuses on the point of view of the attacker. We have designed a new attack framework based on Markov chain theory. The goal of this latter is to maximize the success probability of attacks while minimizing energy consumption. We tested this framework on both passive and active jamming attacks, thus exhibiting the effectiveness of our approach when compared against basic eavesdropping and jamming attacks. We show that this new type of attack can cut by half the energy consumption of an eavesdropping attack, while maintaining 88 percent attack efficiency.