Abstract:
Intrusion Detection Systems (IDS) are security components that could serve IoT security. They do, however, face challenges in terms of autonomy, scalability, and efficiency. The pressing question is how to make the IDS extract the correct network's behavior while being intelligent enough to detect new intrusions. It is, therefore, essential to explore new possibilities that could lead to further improvement in the efficiency of these systems. Introducing particle swarm optimization in intrusion detection systems is a way to approach the problem differently. In this paper, we explain the combination of two techniques, machine learning, a field that provides robust methods for learning and knowledge extraction, and particle systems that include collaborative heuristics for search and detection. There is a shortage of researches which have addressed the IoT intrusion detection problem based on this combination. We will try to fill the gap and discuss the aspects to consider for implementing particle swarm clustering method for intrusion detection in IoT.
Page(s):
2799-2810
DOI:
DOI not available
Published:
Journal: Journal of Theoretical and Applied Information Technology, Volume: 100, Issue: 9, Year: 2022
Keywords:
machine learning
,
IoT
,
intrusion detection
,
Clustering
,
Particle Swarm Optimization
,
Particle systems