Timely detection and mitigation of IoT-based cyberattacks in the smart grid |
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| Authors: | Yasin Y?lmaz Suleyman Uludag |
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| Institution: | 1. Department of Electrical Engineering, University of South Florida, Tampa, FL, USA;2. Department of Computer Science, Engineering and Physics, University of Michigan, Flint, MI, USA;1. Department of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China;2. Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom;3. School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China;4. Hangzhou Zhongheng Cloud Energy Internet Technology Co., Ltd., Hangzhou 310053, China;1. State Key Laboratory of Integrated Service Networks, Xidian University, Xi''an 710071 China;2. Key Laboratory of the Ministry of Education for Wide Band-Gap Semiconductor Materials and Devices, Xidian University, Xi''an 710071 China;1. College of Information Science and Engineering, Northeastern University, Shenyang 110819, PR China;2. College of Sciences, Northeastern University, Shenyang 110004, PR China;1. School of Mechanical and Electrical Engineering and Automation, Shanghai University, Shanghai 200072, China;2. School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China;3. College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023, China |
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| Abstract: | The ongoing changes, updates, and upgrades of the Smart Grid infrastructure open up new cybersecurity challenges whose successful and satisfactory handling is a vital necessity for a viable future of these initiatives. The characteristic of the Smart Grid that leads to physical damage and cascading power failures amplifies the severity of security breaches. A set of recent successful Distributed Denial-of-Service (DDoS) attacks on the Internet, facilitated by the proliferation of the Internet-of-Things (IoT) powered botnets, shows that the Smart Grid may become the target and likely victim of such an attack, potentially leaving catastrophic outage of power service to millions of people. In this paper, under a hierarchical data collection infrastructure we propose a general and scalable mitigation approach, called Minimally Invasive Attack Mitigation via Detection Isolation and Localization (MIAMI-DIL), based on an online and nonparametric anomaly detection algorithm which is scalable and capable of timely detection. We provide a proof-of-concept by means of simulations to show the efficacy and scalability of the proposed approach. |
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