Research Area: Machine Learning
Malicious attack detection is one of the critical cyber-security challenges in the peer-to-peer smart grid platforms due to the fact that attackers’ behaviours change continuously over time. In this paper, we evaluate twelve Machine Learning (ML) algorithms in terms of their ability to detect anomalous behaviours over the networking practice. The evaluation is performed on three publicly available datasets: CICIDS-2017, UNSW-NB15 and the Industrial Control System (ICS) cyber-attack datasets. The experimental work is performed through the ALICE high-performance computing facility at the University of Leicester. Based on these experiments, a comprehensive analysis of the ML algorithms is presented. The evaluation results verify that the Random Forest (RF) algorithm achieves the best performance in terms of accuracy, precision, Recall, F1-Score and Receiver Operating Characteristic (ROC) curves on all these datasets. It is worth pointing out that other algorithms perform closely to RF and that the decision regarding which ML algorithm to select depends on the data produced by the application system.
Keywords:
Cyber Security
intrusion detection
anomaly detection
machine learning
deep learning
smart grid
Author(s) Name: Nebrase Elmrabit; Feixiang Zhou; Fengyin Li; Huiyu Zhou
Journal name:
Conferrence name: 2020 International Conference on Cyber Security and Protection of Digital Services (Cyber Security)
Publisher name: IEEE
DOI: 10.1109/CyberSecurity49315.2020.9138871
Volume Information:
