Identification (biology)

Physics

Attack surface

Biology

Optics

Machine learning

Artificial intelligence

Psychology

Focus (optics)

Data science

Popularity

Adversarial machine learning

Computer science

Botany

Adversarial system

Computer security

Social psychology

Internet of Things

IEEE Communications Surveys & Tutorials

The Internet of Things (IoT) has continued gaining in popularity and importance in everyday life in recent years. However, this development does not only present advantages. Indeed, due to the number of sensitive and private data produced by IoT systems, they have become the new privileged targets for cyberattackers. At the same time, Machine Learning (ML) has gained a phenomenal success in various fields like telecommunications, transport or cybersecurity. Nonetheless, the application of ML can cause significant damage when put in the hands of an attacker. Contrary to many previous works, we do not focus on the potential contributions of ML in the IoT security systems. Indeed, this survey aims to provide a comprehensive overview of ML approaches to enable more effective and less detectable attacks. Thereby, the purpose of this article is to identify and discuss the advantages of the elaboration of ML attacks and the possible solutions already evoked in the literature. Firstly, we provide an identification of the main threats and potential attacks on IoT networks. Then, we investigate on cyberattacks integrating machine learning algorithms during the last few years and we provide future research directions, especially for jamming, side channel, false data injection and adversarial machine learning attacks.

How Machine Learning Changes the Nature of Cyberattacks on IoT Networks: A Survey