Cyber-Physical Security and Resilience via Auditing Cyber and Energy Signals

Introduction and Description

If cyber networks are viewed as the nervous system of infrastructures, then energy networks can be said as the circulatory system of infrastructures. Today, almost everything (such as home appliances, industrial machines, data centers and electrified transportation) is connected with energy networks and draws energy from it. To date, much attention has been paid to data and information in cyber networks, but little attention has been paid to the information embedded in raw electrical waveforms and signals in energy networks. The meter and PMU data used by power engineering are basically a summary of raw waveform data in a time period. There are much more to be explored from raw electrical waveforms and signals of energy networks. For example, all devices in power networks must leave traces of their operation status and health (including faults or attacks) information in the raw electrical waveforms and signals: a cyber-device in fault or under attack will cause unusual energy consumption pattern in power networks; a power electronics or electric machine in fault or under attack may cause unusual harmonics or energy profile in power networks. Also, the weather or geomagnetic events may also leave a trace in those raw waveform data. Therefore, we can potentially use the electrical waveform and signals to (1) enable fault monitoring, diagnosis and prognosis of power electronics and electric machines; (2) enable detection, identification and defense of cyber and physical attacks in both cyber and physical world. The possibility may be well beyond what we can imagine now. It broadly applies to many cyber-physical systems and applications, such as smart grid, manufacturing systems, building systems, electrical vehicles.

Figure 1. A big picture of the general learning based IoT security system

Figure 2. Left: The power meter readings from a (a) normal and (b) abnormal system; Right: Undesirable harmonics caused by attacks in the power network of IoT systems.

Figure 3: Electrical waveform signatures of faults and attacks in power networks

Figure 4. Left: Real-time attack detection interface; Right: Testbed of the attack detection system

Transformative Characteristics

Informatics and security in energy networks are fundamental and transformative, as any cyber and physical (including weather) attacks or faults must leave a trace in energy networks. This may require to first build a high-fidelity modeling of energy networks. Traditional power network modeling often at macro scale, but little work has been done to build a high fidelity power network model to include power electronics physical models at micro scale; Traditional diagnosis of power electronics and electric machines, which does not consider potential attacks, are based on physical models or sensors, but little has been done to detect anomaly (faults or attacks) by investigating the signals and electric waveforms in power networks.

National Need/Grand Challenge

According to statistics from US Department of Energy, 80% of U.S. electricity is expected to  flow through power electronics by 2030. Due to the lack of the awareness, power electronics converters in energy networks are vulnerable to attack once connected to internet. Once attacked, it will bring dramatic damages on many safety-critical areas including transportation, energy, and military services. Therefore, there is a national need for US to address this grand challenge.

Informatics and security in energy networks requires an interdisciplinary collaboration and leadership of multiple engineering disciplines, as it may require transformative research on power electronics, electrical engineering, computer science and engineering (including machine learning), cyber and physical security, and statistics and big data.

Faculty

WenZhan Song
Jin Ye

Postdoctoral Associates

Fangyu Li

Students

Yang Shi
Zhiwei Luo
Maohua Liu
Bowen Yang

Publications

Bowen Yang; Lulu Guo; Fangyu Li; Jin Ye; Wenzhan Song Vulnerability Assessments of Electric Drive Systems due to Sensor Data Integrity Attacks Journal Article IEEE Transactions on Industrial Informatics , 2019.

Fangyu Li; Rui Xie; Bowen Yang; Lulu Guo; Ping Ma; Jianjun Shi; Jin Ye; WenZhan Song. Detection and Identification of Cyber and Physical Attacks on Distribution Power Grids with PVs: An Online High-Dimensional Data-driven Approach. Journal of Emerging and Selected Topics in Power Electronics, 2019.

Fangyu Li; Yang Shi; Aditya Shinde; Jin Ye; WenZhan Song Enhanced Cyber-physical Security in Internet of Things through Energy Auditing Journal Article IEEE Internet of Things Journal, 2019.

Fangyu Li; Aditya Shinde; Yang Shi; Jin Ye; Xiang-Yang Li; WenZhan Song, System Statistics Learning-Based IoT Security: Feasibility and Suitability Journal Article, IEEE Internet of Things Journal, 2019.

Bowen Yang; Lulu Guo; Fangyu Li; Jin Ye; Wenzhan Song Impact Analysis of Data Integrity Attacks on Power Electronics and Electric Drives Conference 2019 IEEE Transportation Electrification Conference & Expo, 2019.

Bowen Yang; Fangyu Li; Jin Ye; Wenzhan Song Condition Monitoring and Fault Diagnosis of Generators in Power Networks Conference IEEE Power & Energy Society General Meeting, 2019.

Liu Pengfei; Yang Panlong; Song WenZhan; Yan Yubo; Li Xiang-Yang Real-time Identification of Rogue WiFi Connections Using Environment-Independent Physical Features Conference IEEE International Conference on Computer Communications (INFOCOM), 2019.

Minhui Zou; Chengliang Wang; Fangyu Li; WenZhan Song Network Phenotyping for Network Traffic Classification and Anomaly Detection Conference 2018 IEEE International Symposium on Technologies for Homeland Security (HST), 2018.

Song Tan; Debraj De; WenZhan Song; Junjie Yang; Sajal Das Survey of Security Advances in Smart Grid: A Data Driven Approach Journal Article IEEE Communications Surveys and Tutorials, 18 (1), pp. 397-422, 2017.

Song Tan; Wen-Zhan Song; Michael Stewart; Junjie Yang; Lang Tong Online Data Integrity Attacks Against Real-Time Electrical Market in Smart Grid Journal Article IEEE Transaction on Smart Grid, 2016.

Song Tan; WenZhan Song; Michael Stewart; Lang Tong Construct Data Integrity Attacks Against Real-Time Electrical Market in Smart Grid Conference IEEE International Conference on Smart Grid Communications (SmartGridComm), 2015.

Paritosh Ramanan; Goutham Kamath; Wen-Zhan Song NetTomo: A Tomographic approach towards Network Diagnosis Conference IEEE International Symposium on a World of Wireless Mobile and Multimedia Networks (WoWMoM), 2015.

Song Tan; Wen-Zhan Song; Michael Stewart; Lang Tong LPAttack: Leverage Point Attacks against State Estimation in Smart Grid Conference IEEE Globe Communications Conference (GlobeCom), 2014.

Liang Zhao; Wen-Zhan Song Distributed Power-line Outage Detection Based on Wide Area Measurement System Journal Article Sensors, 2014.

Liang Zhao; Wen-Zhan Song A New Multi-objective Microgrid Restoration Via Semidefinite Programming Conference 33rd International Performance Computing and Communications Conference (IEEE IPCCC), 2014.

Liang Zhao; Wen-Zhan Song; Lang Tong; Yuan Wu Monitoring for Power-line Change and Outage Detection in Smart Grid via the Alternating Direction Method of Multipliers Conference The 28th IEEE International Conference on Advanced Information Networking and Applications Workshops, 2014.

Liang Zhao; Wen-Zhan Song; Lang Tong; Yuan Wu; Junjie Yang Topology Identification in Smart Grid with Limited Measurements Via Convex Optimization Conference 2014 IEEE Innovative Smart Grid Technologies Conference- Asia, 2014.