Grammatical Evolution for Detecting Cyberattacks in Internet of Things Environments

Hasanen Alyasiri, University of Kufa, Iraq
John Clark, The University of Sheffield, United Kingdom
Ali Malik, Technological University Dublin
Ruairí de Fréin, Technological University Dublin

Document Type Conference Paper

The 30th International Conference on Computer Communications and Networks (ICCCN 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 systems 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.