TY - GEN
T1 - Securing Power Distribution Grid Against Power Botnet Attacks
AU - Wang, Lizhi
AU - Pepin, Lynn
AU - Li, Yan
AU - Miao, Fei
AU - Herzberg, Amir
AU - Zhang, Peng
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - A botnet is a collection of internet-facing devices that are compromised and controlled by a malicious hacker. In this paper, we propose an attack utilising a botnet of high-wattage internet-facing devices, which we call a power botnet. Power botnet attacks can decrease the reliability of power supply, damage the power quality and even cause catastrophic consequences in power distribution grid. To study the effects on power distribution systems, we simulate three different types of power botnet attacks using OpenDSS, and show the change of OLTC lifespans under attacks. We then use deep learning methods to detect these attacks. We show successful detection for two of these attacks and a low detection rate for the third attack. To the best of our knowledge, this is the first paper to consider power botnet attacks, and leverage deep learning methods to detect these attacks on power distribution grids. Future work such as detection schemes for more complicated power botnet attacks will be developed based on the results of this work.
AB - A botnet is a collection of internet-facing devices that are compromised and controlled by a malicious hacker. In this paper, we propose an attack utilising a botnet of high-wattage internet-facing devices, which we call a power botnet. Power botnet attacks can decrease the reliability of power supply, damage the power quality and even cause catastrophic consequences in power distribution grid. To study the effects on power distribution systems, we simulate three different types of power botnet attacks using OpenDSS, and show the change of OLTC lifespans under attacks. We then use deep learning methods to detect these attacks. We show successful detection for two of these attacks and a low detection rate for the third attack. To the best of our knowledge, this is the first paper to consider power botnet attacks, and leverage deep learning methods to detect these attacks on power distribution grids. Future work such as detection schemes for more complicated power botnet attacks will be developed based on the results of this work.
UR - http://www.scopus.com/inward/record.url?scp=85079048108&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85079048108&partnerID=8YFLogxK
U2 - 10.1109/PESGM40551.2019.8974098
DO - 10.1109/PESGM40551.2019.8974098
M3 - Conference contribution
AN - SCOPUS:85079048108
T3 - IEEE Power and Energy Society General Meeting
BT - 2019 IEEE Power and Energy Society General Meeting, PESGM 2019
PB - IEEE Computer Society
T2 - 2019 IEEE Power and Energy Society General Meeting, PESGM 2019
Y2 - 4 August 2019 through 8 August 2019
ER -