| Title | Cyber Security Enhancement of Smart Grids Via Machine Learning - A Review |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Rao, Poojith U., Sodhi, Balwinder, Sodhi, Ranjana |
| Conference Name | 2020 21st National Power Systems Conference (NPSC) |
| Keywords | compositionality, Deep Learning, Detectors, Human Behavior, human factors, Internet of Things, machine learning, Monitoring, pubcrawl, reinforcement learning, resilience, Resiliency, security, Signal processing, Signal processing algorithms, smart grid cyber security, Smart Grid Sensors, Smart grids |
| Abstract | The evolution of power system as a smart grid (SG) not only has enhanced the monitoring and control capabilities of the power grid, but also raised its security concerns and vulnerabilities. With a boom in Internet of Things (IoT), a lot a sensors are being deployed across the grid. This has resulted in huge amount of data available for processing and analysis. Machine learning (ML) and deep learning (DL) algorithms are being widely used to extract useful information from this data. In this context, this paper presents a comprehensive literature survey of different ML and DL techniques that have been used in the smart grid cyber security area. The survey summarizes different type of cyber threats which today's SGs are prone to, followed by various ML and DL-assisted defense strategies. The effectiveness of the ML based methods in enhancing the cyber security of SGs is also demonstrated with the help of a case study. |
| DOI | 10.1109/NPSC49263.2020.9331859 |
| Citation Key | rao_cyber_2020 |
Cyber Security Enhancement of Smart Grids Via Machine Learning - A Review