Full Program
Summary:
The rapid expansion of Internet of Things (IoT) systems has expanded the threat surface as well as the increased likelihood of cyber-attacks that can result in Denial-of-Service (DoS) in the system operation. Prominent attacks leading to DoS belong to the blackhole flooding sinkhole and spoofing categories. The detection of these attacks though still remains a great challenge which necessitates the presence of more accurate and robust detection methods. To this end in this paper we investigate the use of Machine Learning (ML) models to perform cross-layer detection and compare them with single-layer detection approaches across several IoT systems. In our analysis we evaluate performance metrics (accuracy precision recall F1-score) across all attacks. Results reveal that cross-layer methodologies consistently outperform single-layer detection achieving higher accuracy and almost all cases lower variability. Additionally feature selection is another advantage of cross-layer methodologies with multi-layered datasets enabling more robust detection compared to single-layer approaches.Author(s):
Dimitrios Tasiopoulos
University of Thessaly
Greece
Apostolis Xenakis
University of Thessaly
Greece
Alexios Lekidis
University of Thessaly
Greece
Dimitrios Kosmanos
Greece
Costas Chaikalis
University of Thessaly
Greece
Vasileios Vlachos
University of Thessaly
Greece