Deep Reinforcement Learning for Penetration Testing of Cyber-Physical Attacks in the Smart Grid
Author:
Affiliation:
1. Concordia Institute for Information Systems Engineering (CIISE), Concordia University,Montreal,Canada
2. Global Artificial Intelligence Accelerator (GAIA),Ericsson,Montreal,Canada
Funder
Natural Sciences and Engineering Research Council of Canada
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9891857/9889787/09892584.pdf?arnumber=9892584
Reference26 articles.
1. Coordinated Topology Attacks in Smart Grid Using Deep Reinforcement Learning
2. Evaluation of Reinforcement Learning-Based False Data Injection Attack to Automatic Voltage Control
3. Reinforcement Learning Based Penetration Testing of a Microgrid Control Algorithm
4. Autonomous penetration testing using reinforcement learning;schwartz;ArXiv Preprint,2019
5. Automated Penetration Testing Using Deep Reinforcement Learning
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1. Knowledge-Informed Auto-Penetration Testing Based on Reinforcement Learning with Reward Machine;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30
2. Opportunities and Challenges of Using Artificial Intelligence in Securing Cyber-Physical Systems;Artificial Intelligence for Security;2024
3. A Reinforcement-Learning-based Agent to discover Safety-Critical States in Smart Grid Environments;2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME);2023-07-19
4. Deterring Adversarial Learning in Penetration Testing by Exploiting Domain Adaptation Theory;2023 Systems and Information Engineering Design Symposium (SIEDS);2023-04-27
5. Privacy and Security Control Approach for DDoS Attacks in Cyber Physical Systems using Deep Learning;2023 2nd International Conference for Innovation in Technology (INOCON);2023-03-03
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