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Autonomous Tools for Attack Surface Reduction

Technical Report ·
DOI:https://doi.org/10.2172/1773387· OSTI ID:1773387
 [1];  [2];  [3];  [4];  [5];  [6];  [6];  [3];  [4];  [7];  [5];  [7]
  1. Iowa State Univ., Ames, IA (United States); Iowa State University
  2. Washington State Univ., Pullman, WA (United States)
  3. General Electric Co., Richland, WA (United States)
  4. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  5. Argonne National Lab. (ANL), Argonne, IL (United States)
  6. Iowa State Univ., Ames, IA (United States)
  7. Cedar Falls Utilities, Cedar Falls, IA (United States)
+ Show Author Affiliations

The electric power grid is a complex critical infrastructure that forms the lifeline of modern society, and its secure and reliable operation is of paramount importance to national security and economic well being. However, recent findings documented in authoritative sources indicate the threat of cyber-based attacks growing in numbers and sophistication. However, securing the grid against stealthy cyber attacks is a challenging task due to legacy nature of the infrastructure coupled with dynamic nature of threat landscape and ever growing sophistication of the adversaries. Additionally, the grid’s attack surface continues to grow with the increased dependence on digital communications and control that now extends to each consumer through smart meters and distributed energy resources. Unfortunately, this expansive surface increases the grid’s vulnerability and further exposes critical control systems in both substations and control centers. To respond to this emerging need, we had successfully assembled an interdisciplinary team with academic- industry partnership to successfully conduct research, development, evaluation, demonstration, and commercialization of attack surface reduction tools, whose goal is to significantly reduce the cyber attack surface in the North American power grid. Our proposed project was a synergistic collaborative effort leveraging the synergistic expertise of the team members across power systems, cyber security and CPS security, testbeds, field deployments and demonstration, and successful commercialization. The team consisted of leading experts from two major universities – Iowa State University, Washington State University – complemented by reputed researchers from two DOE national laboratories – Pacific Northwest National Lab, and Argonne National Lab, one major utility vendor GE Global Research, and one utility partner – Cedar Falls Utilities (CFU). The team members have proven track record of successful academic-industry collaboration in interdisciplinary R&D projects, and bring onboard some of the best state-of-the-art testbed resources, industry-grade SCADA/EMS/DMS environment for experimentation and field demonstration.

Research Organization:
Iowa State Univ., Ames, IA (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
DOE Contract Number:
OE0000830
OSTI ID:
1773387
Report Number(s):
DOE-ISU--00830
Country of Publication:
United States
Language:
English

References (14)

A Hierarchical Multi-Agent Based Anomaly Detection for Wide-Area Protection in Smart Grid conference August 2018
A symmetric address translation approach for a network layer moving target defense to secure power grid networks conference September 2017
Cyber-Physical Smart Light Control System Integration with Smart Grid using Zigbee conference February 2020
A Cyber-Physical Anomaly Detection for Wide-Area Protection using Machine Learning journal January 2021
Anomaly Detection and Mitigation for Wide-Area Damping Control using Machine Learning journal January 2020
Testbed-based Evaluation of SIEM Tool for Cyber Kill Chain Model in Power Grid SCADA System conference October 2019
A Novel Architecture for Attack-Resilient Wide-Area Protection and Control System in Smart Grid conference October 2020
Coherency-Based Detection Algorithm for Synchrophasor Cyberattacks conference October 2019
Evaluation of Anomaly Detection for Wide-Area Protection Using Cyber Federation Testbed conference August 2019
Anomaly Detection for Power System Generation Control based on Hierarchical DBSCAN conference September 2018
Efficient Modeling of HIL Multi-Grid System for Scalability & Concurrency in CPS Security Testbed conference October 2019
Application of Chebyshev’s Inequality in Online Anomaly Detection Driven by Streaming PMU Data conference August 2020
Decision Tree Based Anomaly Detection for Remedial Action Scheme in Smart Grid using PMU Data conference August 2018
Security Evaluation of Two Intrusion Detection Systems in Smart Grid SCADA Environment conference September 2018

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Related Subjects

24 POWER TRANSMISSION AND DISTRIBUTION
42 ENGINEERING
Cybersecurity
Grid