| Title | Cyber-Security Enhancement of Smart Grid's Substation Using Object's Distance Estimation in Surveillance Cameras |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Ahmadian, Saeed, Ebrahimi, Saba, Malki, Heidar |
| Conference Name | 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC) |
| Keywords | Conferences, Convolutional codes, cyber-security, detection algorithms, fully convolutional deep network, human factors, Meters, Metrics, pubcrawl, resilience, risk assessment, Scalability, Security Risk Estimation, Streaming media, Substations, surveillance, threat detection, YOLO |
| Abstract | Cyber-attacks toward cyber-physical systems are one of the main concerns of smart grid's operators. However, many of these cyber-attacks, are toward unmanned substations where the cyber-attackers needs to be close enough to substation to malfunction protection and control systems in substations, using Electromagnetic signals. Therefore, in this paper, a new threat detection algorithm is proposed to prevent possible cyber-attacks toward unmanned substations. Using surveillance camera's streams and based on You Only Look Once (YOLO) V3, suspicious objects in the image are detected. Then, using Intersection over Union (IOU) and Generalized Intersection Over Union (GIOU), threat distance is estimated. Finally, the estimated threats are categorized into three categories using color codes red, orange and green. The deep network used for detection consists of 106 convolutional layers and three output prediction with different resolutions for different distances. The pre-trained network is transferred from Darknet-53 weights trained on 80 classes. |
| DOI | 10.1109/CCWC51732.2021.9375989 |
| Citation Key | ahmadian_cyber-security_2021 |
Cyber-Security Enhancement of Smart Grid's Substation Using Object's Distance Estimation in Surveillance Cameras