Document Type

Conference Paper


Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence


Computer Sciences, Communication engineering and systems, telecommunications

Publication Details

  • Conference: IEEE ICCCN
  • At: Athens, Greece, July 2021


The Internet of Things (IoT) is revolutionising nearly every aspect of modern life, playing an ever greater role in both industrial and domestic sectors. The increasing frequency of cyber-incidents is a consequence of the pervasiveness of IoT. Threats are becoming more sophisticated, with attackers using new attacks or modifying existing ones. Security teams must deal with a diverse and complex threat landscape that is constantly evolving. Traditional security solutions cannot protect such sys- tems adequately and so researchers have begun to use Machine Learning algorithms to discover effective defence systems. In this paper, we investigate how one approach from the domain of evolutionary computation - grammatical evolution - can be used to identify cyberattacks in IoT environments. The experiments were conducted on up-to-date datasets and compared with state- of-the-art algorithms. The potential application of evolutionary computation-based approaches to detect unknown attacks is also examined and discussed


Science Foundation Ireland (SFI)