| Title | A Sequential Supervised Machine Learning Approach for Cyber Attack Detection in a Smart Grid System |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Farrukh, Yasir Ali, Ahmad, Zeeshan, Khan, Irfan, Elavarasan, Rajvikram Madurai |
| Conference Name | 2021 North American Power Symposium (NAPS) |
| Date Published | nov |
| Keywords | class imbalance problem, Classification algorithms, composability, compositionality, Computational modeling, Cyber Dependencies, cyberattack, Deep Learning, Human Behavior, intrusion detection system, machine learning, machine learning algorithms, Metrics, pubcrawl, reliability, resilience, Resiliency, Scalability, smart grid system, Smart grids, supervised learning, Training |
| Abstract | Modern smart grid systems are heavily dependent on Information and Communication Technology, and this dependency makes them prone to cyber-attacks. The occurrence of a cyber-attack has increased in recent years resulting in substantial damage to power systems. For a reliable and stable operation, cyber protection, control, and detection techniques are becoming essential. Automated detection of cyberattacks with high accuracy is a challenge. To address this, we propose a two-layer hierarchical machine learning model having an accuracy of 95.44 % to improve the detection of cyberattacks. The first layer of the model is used to distinguish between the two modes of operation - normal state or cyberattack. The second layer is used to classify the state into different types of cyberattacks. The layered approach provides an opportunity for the model to focus its training on the targeted task of the layer, resulting in improvement in model accuracy. To validate the effectiveness of the proposed model, we compared its performance against other recent cyber attack detection models proposed in the literature. |
| DOI | 10.1109/NAPS52732.2021.9654767 |
| Citation Key | farrukh_sequential_2021 |
A Sequential Supervised Machine Learning Approach for Cyber Attack Detection in a Smart Grid System