* Acknowledgement:

“This material is based upon work supported by the National Science Foundation under Grant No. CNS-1545037.”

* Disclaimer:

"Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.” 

* Award numbers (collaborative awards):

https://www.nsf.gov/awardsearch/showAward?AWD_ID=1545037 

Duration (expected): September 1, 2015 – August 31, 2020

Award Title: CPS: Breakthrough: Collaborative Research: Securing Smart Grid by Understanding Communications Infrastructure Dependencies

PI: Sajal K. Das

Co-PIs: Mariesa Crow and Simone Silvestri

Postdoc: Shameek Bhattacharjee

Student(s): Venkat Praveen Madhavarapu and Prithwiraj Roy

  • Project Goals:

 Smart grid includes two interdependent infrastructures: power transmission and distribution network, and the supporting telecommunications network. Complex interactions among these infrastructures lead to new pathways for attack and failure propagation that are currently not well understood. This innovative project takes a holistic multilevel approach to understand and characterize the interdependencies between these two infrastructures, and devise mechanisms to enhance their robustness.


Specifically, the project has four major goals. 

    1. Identify advanced persistent threat (e.g., data integrity and availability attacks) strategies in smart grid Advanced Metering Infrastructure (AMI) from organized adversaries. 
    2. Develop a novel lightweight anomaly detection method that captures early indication of incidence, type, strength and strategy of various attacks with low false alarm rates and  high detection accuracy. Subsequently, develop a threat mitigation scheme to defend according changing attack contexts.
    3.  A unique aspect of our smart grid security project is the critical importance of timeliness, and thus a trade off between effectiveness of the mechanisms and the overhead introduced. The project is expected to provide practical techniques for making the smart grid more robust against failures and attacks, and enable it to recover from large scale failures with less loss of capacity. 
    4. The project will also train postdocs, graduate and undergraduate students in the multidisciplinary areas of power systems operation and design, networking protocols, and cyber-physical security.
  • Major Findings:

The project has made substantial progress by developing a novel framework for anomaly detection and trust based scoring model to defend against data falsification or integrity attacks in smart grid's AMI infrastructure. This project already led to several high quality journal and peer-reviewed conference papers. 

  • We proposed a light-weight, privacy-preserving anomaly detection scheme for identifying compromised smart meters launching energy consumption data falsification attacks regardless of the fraction of compromised meters. The proposed scheme captures various kinds of data falsification attacks (e.g., additive, deductive and camouflage) and data omission attacks launched by powerful organized adversaries with a low margin of false data per meter. It yields low false alarm rates even when 70% of the smart meters are compromised. Specifically, for decentralized implementation of the anomaly detector, we can detect data falsification of as low as 50W-120W from micro-grid sizes varying between 200 or 5000 meters with only one false alarm in a year-long testing set. We have validated the proposed approach through detailed mathematical analysis and experimenting with real world AMI energy consumption datasets from different countries over different years. We also analyzed the sensitivity and scalability trade-off for our anomaly detection scheme. 
  • A high quality journal paper has been accepted in the IEEE Transactions on Dependable and Secure Computing. This work proposes a novel data-driven anomaly detection scheme that detects and mitigates data integrity and availability attacks launched by stealthy advanced persistent adversaries regardless of cyber or physical exploits and is agnostic of the attack injection point in the AMI network.
  • A high quality journal paper is under minor review in the ACM Transactions on Privacy and Security. This work proposes a trust model for smart meters embedded with attack context to identify attacks evolving over multiple spatial and temporal scales and are opportunistic and granular in nature. 
  • A high quality journal paper dealing with realistic model for failure Propagation in interdependent cyber-physical systems (e.g., electric grip coupled with communication networks) has been accepted in IEEE Transactions on Network Science and Engineering, in the Special Issue on Network Science for High-Confidence Cyber­Physical Systems. 
  • A high quality paper on challenged networks applicable to CPS, is published in IEEE INFOCOM 2019, a flagship conference in networking with about 15% acceptance rate. 
  • An excellent paper was published in ACM ASIACCS 2018, a very competitive SIGSAC conference having acceptance rate of 17%. This work deals with real time identification of compromised smart meters launching data falsification attacks with various attack strengths and strategies.

* Publications (Selected List):

* Broader Impacts

 The project develops practical schemes to identify compromised nodes (e.g., smart meters) in order to reduce the impact of attacks and failures, and dynamic defense mechanisms to address increasingly popular persistent attacks in smart grid. This results in practical tools for assessing vulnerability and threats and their impact propagation.  Experimental study with real world datasets of energy consumption in smart grid provides the involved students hands-on training and technology transfer opportunities. In addition, the research conducted in this project motivates students, including female and under-represented minority, to choose careers in the nationally important area of cyber-physical security.

A postdoc working on this project and being mentored by the PI became an Assistant Professor at Western Michigan University in fall 2018. Two Ph.D. students are being trained. An undergraduate student supported by the REU supplement, has joined our Ph.D. program starting fall 2019.

* Point of Contact: Sajal K. Das (sdas@mst.edu)

* Date of last Update: August 31, 2019