| Title | False Data Injection Impact Analysis In AI-Based Smart Grid |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Tufail, Shahid, Batool, Shanzeh, Sarwat, Arif I. |
| Conference Name | SoutheastCon 2021 |
| Keywords | Analytical models, composability, Cyber Attacks, Data models, data streaming, False Data Detection, False Data Injection, Human Behavior, Intrusion detection, Knowledge engineering, multi layer perceptron, Multi-factor authentication, Predictive models, pubcrawl, resilience, Resiliency, Smart grid, Training |
| Abstract | As the traditional grids are transitioning to the smart grid, they are getting more prone to cyber-attacks. Among all the cyber-attack one of the most dangerous attack is false data injection attack. When this attack is performed with historical information of the data packet the attack goes undetected. As the false data is included for training and testing the model, the accuracy is decreased, and decision making is affected. In this paper we analyzed the impact of the false data injection attack(FDIA) on AI based smart grid. These analyses were performed using two different multi-layer perceptron architectures with one of the independent variables being compared and modified by the attacker. The root-mean squared values were compared with different models. |
| DOI | 10.1109/SoutheastCon45413.2021.9401940 |
| Citation Key | tufail_false_2021 |
False Data Injection Impact Analysis In AI-Based Smart Grid