DERauth: A Battery-Based Authentication Scheme for Distributed Energy Resources

Over the past decades, power systems have experienced drastic transformations in order to address the growth in energy demand, reduce carbon emissions, and enhance power quality and energy efficiency. This shift to the smart grid concept involves, among others, the utilization of distributed energy resources (DERs) such as rooftop solar panels and storage systems, contributing towards grid decentralization while improving control over power generation. In order to seamlessly integrate DERs into power systems, embedded devices are used to support the communication and control functions of DERs. As a result, vulnerabilities of such components can be ported to the industrial environment. Insecure control networks and protocols further exacerbate the problem. Towards reducing the attack surface, we present an authentication scheme for DERs, DERauth, which leverages the inherent entropy of the DER battery energy storage system (BESS) as a root-of-trust. The DER authentication is achieved using a challenge-reply mechanism that relies on the corresponding DER's BESS state-of-charge (SoC) and voltage measurements. A dynamically updating process ensures that the BESS state is up-to-date. We evaluate our proof-of-concept in a prototype development that uses lithium-ion (li-ion) batteries for the BESS. The robustness of our design is assessed against modeling attacks performed by neural networks.

[1]  Daniel E. Holcomb,et al.  Power-Up SRAM State as an Identifying Fingerprint and Source of True Random Numbers , 2009, IEEE Transactions on Computers.

[2]  Ahmad-Reza Sadeghi,et al.  INVITED: In Hardware We Trust : Gains and Pains of Hardware-assisted Security , 2019, 2019 56th ACM/IEEE Design Automation Conference (DAC).

[3]  Michail Maniatakos,et al.  Malicious Firmware Detection with Hardware Performance Counters , 2016, IEEE Transactions on Multi-Scale Computing Systems.

[4]  Eduardo Chielle,et al.  PHYLAX: Snapshot-based profiling of real-time embedded devices via JTAG interface , 2018, 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[5]  Jeroen Delvaux,et al.  Machine-Learning Attacks on PolyPUFs, OB-PUFs, RPUFs, LHS-PUFs, and PUF–FSMs , 2019, IEEE Transactions on Information Forensics and Security.

[6]  Kang Xu,et al.  Electrochemical impedance study on the low temperature of Li-ion batteries , 2004 .

[7]  Aeo,et al.  Annual Energy Outlook 2008: With Projections to 2030 , 2008 .

[8]  A. B. M. Omar Faruk Testing and Exploring Vulnerabilities of the Applications Implementing DNP3 Protocol , 2008 .

[9]  Jorge Guajardo,et al.  MEMS Gyroscopes as Physical Unclonable Functions , 2016, CCS.

[10]  Carson Labrado,et al.  Design of a Piezoelectric-Based Physically Unclonable Function for IoT Security , 2019, IEEE Internet of Things Journal.

[11]  Cher Ming Tan,et al.  Effect of Temperature on the Aging rate of Li Ion Battery Operating above Room Temperature , 2015, Scientific Reports.

[12]  Solon Falas,et al.  A Hardware-based Framework for Secure Firmware Updates on Embedded Systems , 2019, 2019 IFIP/IEEE 27th International Conference on Very Large Scale Integration (VLSI-SoC).

[13]  Saraju P. Mohanty,et al.  Everything You Wanted to Know About PUFs , 2017, IEEE Potentials.

[14]  Victor C. M. Leung,et al.  Multilayer Consensus ECC-Based Password Authenticated Key-Exchange (MCEPAK) Protocol for Smart Grid System , 2013, IEEE Transactions on Smart Grid.

[15]  Jean-Pierre Seifert,et al.  Physical Characterization of Arbiter PUFs , 2014, IACR Cryptol. ePrint Arch..

[16]  Gaurang Panchal,et al.  Behaviour Analysis of Multilayer Perceptrons with Multiple Hidden Neurons and Hidden Layers , 2011 .

[17]  S. Williamson,et al.  A Five-Parameter Analytical Curvefit Model for Open-Circuit Voltage Variation with State-of-Charge of a Rechargeable Battery , 2018, 2018 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES).

[18]  Cas J. F. Cremers,et al.  Secure Authentication in the Grid: A Formal Analysis of DNP3: SAv5 , 2017, ESORICS.

[19]  Mario Cacciato,et al.  Analysis of state of charge estimation methods for smart grid with VRLA batteries , 2017 .

[20]  Michail Maniatakos,et al.  Security analysis of smart grid , 2017 .

[21]  Peter Hall,et al.  Energy-storage technologies and electricity generation , 2008 .

[22]  René Schenkendorf,et al.  Model‐Based Uncertainty Quantification for the Product Properties of Lithium‐Ion Batteries , 2020, Energy Technology.

[23]  Sujeet Shenoi,et al.  A Taxonomy of Attacks on the DNP3 Protocol , 2009, Critical Infrastructure Protection.

[24]  Ramesh Karri,et al.  PREEMPT: PReempting Malware by Examining Embedded Processor Traces , 2019, 2019 56th ACM/IEEE Design Automation Conference (DAC).

[25]  Michail Maniatakos,et al.  The Cybersecurity Landscape in Industrial Control Systems , 2016, Proceedings of the IEEE.

[26]  Yu Hu,et al.  Modeling attacks on strong physical unclonable functions strengthened by random number and weak PUF , 2018, 2018 IEEE 36th VLSI Test Symposium (VTS).

[27]  Mathias Payer,et al.  Control-Flow Integrity , 2017, ACM Comput. Surv..

[28]  Sherali Zeadally,et al.  A Survey on Hardware-based Security Mechanisms for Internet of Things , 2019, ArXiv.

[29]  Mohsen Guizani,et al.  Battery Status-aware Authentication Scheme for V2G Networks in Smart Grid , 2013, IEEE Transactions on Smart Grid.