Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | 2020 International Conference on Cyber Security and Protection of Digital Services (Cyber Security) |
| Publisher | IEEE |
| Number of pages | 8 |
| ISBN (Electronic) | 9781728164281 |
| ISBN (Print) | 9781728164298 |
| DOIs | |
| Publication status | Published - Jun 2020 |
| Event | International Conference on Cyber Security and Protection of Digital Services - Online Duration: 15 Jun 2020 → 19 Jun 2020 https://www.c-mric.com/cs2020 (Link to conference website) |
Conference
| Conference | International Conference on Cyber Security and Protection of Digital Services |
|---|---|
| Abbreviated title | Cyber Security 2020 |
| Period | 15/06/20 → 19/06/20 |
| Internet address |
|
Keywords
- cyber security, intrusion detection, anomaly detection, machine learning, deep learning, smart grid
- deep learning
- smart grid
- Cyber Security
- anomaly detection
- intrusion detection
- machine learning
ASJC Scopus subject areas
- Information Systems and Management
- Artificial Intelligence
- Safety, Risk, Reliability and Quality
- Computer Networks and Communications
- Computational Theory and Mathematics
Documents and Links
- Elmrabit, N. et al (2020) Evaluation of machine learning algorithms for anomaly detection
Copyright © 2020 IEEE. This is a post-peer review, pre-copy edited version of the article and may differ slightly from the final published version of the work. This work is made available in line with the publisher policy. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.