@inproceedings{MessaadJeradSikora2021,
  author    = {Mohamed Abou Messaad and Chadlia Jerad and Axel Sikora},
  title     = {AI Approaches for IoT Security Analysis},
  series = {Intelligent Systems, Technologies and Applications. Proceedings of Sixth ISTA 2020, India},
  volume    = {Advances in Intelligent Systems and Computing 1353},
  editor    = {Marcin Paprzycki and Sabu M. Thampi and Sushmita Mitra and Ljiljana Trajkovic and El-Sayed M. El-Alfy},
  publisher = {Springer},
  address   = {Singapore},
  isbn      = {978-981-16-0729-5 (Print)},
  issn      = {2194-5357 (Print)},
  doi       = {10.1007/978-981-16-0730-1\_4},
  pages     = {47 -- 70},
  year      = {2021},
  abstract  = {IoT networks are increasingly used as entry points for cyberattacks, as often they offer low-security levels, as they may allow the control of physical systems and as they potentially also open the access to other IT networks and infrastructures. Existing intrusion detection systems (IDS) and intrusion prevention systems (IPS) mostly concentrate on legacy IT networks. Nowadays, they come with a high degree of complexity and adaptivity, including the use of artificial intelligence. It is only recently that these techniques are also applied to IoT networks. In this paper, we present a survey of machine learning and deep learning methods for intrusion detection, and we investigate how previous works used federated learning for IoT cybersecurity. For this, we present an overview of IoT protocols and potential security risks. We also report the techniques and the datasets used in the studied works, discuss the challenges of using ML, DL and FL for IoT cybersecurity and provide future insights.},
  language  = {en}
}