Network topology risk assessment of stealthy cyber attacks on advanced metering infrastructure networks

Advanced Metering Infrastructure (AMI) plays a crucial role in Demand Side Management (DSM) in Smart Grid systems. It provides real-time, two-way communication capabilities between a utility/load aggregator and consumers. The communication infrastructure, by virtue of topological weaknesses, is vulnerable to cyber attacks that are undetectable or stealthy. This work investigates the topological vulnerabilities of AMI networks that could result in potential theft of electricity through hacked smart meters. In particular, a provably correct risk assessment protocol is proposed to identify completely the individual nodes in mesh network based AMIs that are potential targets of such economically motivated stealthy cyber attacks. The protocol proposed utilizes knowledge of the network topology and data obtained from existing system monitoring technologies. A case study is provided to demonstrate the protocol and its effectiveness.

[1]  Lang Tong,et al.  Impacts of Malicious Data on Real-Time Price of Electricity Market Operations , 2012, 2012 45th Hawaii International Conference on System Sciences.

[2]  Parv Venkitasubramaniam,et al.  The privacy analysis of battery control mechanisms in demand response: Revealing state approach and rate distortion bounds , 2014, 53rd IEEE Conference on Decision and Control.

[3]  Peng Ning,et al.  False data injection attacks against state estimation in electric power grids , 2011, TSEC.

[4]  Massimo Franceschetti,et al.  Random networks for communication : from statistical physics to information systems , 2008 .

[5]  Christian Wietfeld,et al.  A system design framework for scalability analysis of geographic routing algorithms in large-scale mesh networks , 2010, SimuTools.

[6]  Nadeem Javaid,et al.  Minimizing Electricity Theft Using Smart Meters in AMI , 2012, 2012 Seventh International Conference on P2P, Parallel, Grid, Cloud and Internet Computing.

[7]  Christian Wietfeld,et al.  RF Mesh Systems for Smart Metering: System Architecture and Performance , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[8]  Reinhard Diestel,et al.  Graph Theory , 1997 .

[9]  Ying Jun Zhang,et al.  Defending mechanisms against false-data injection attacks in the power system state estimation , 2011, 2011 IEEE GLOBECOM Workshops (GC Wkshps).

[10]  Lang Tong,et al.  On Topology Attack of a Smart Grid: Undetectable Attacks and Countermeasures , 2013, IEEE Journal on Selected Areas in Communications.

[11]  Philip Koopman.  Embedded System Security , 2004, Computer.

[12]  Sushil Jajodia,et al.  Topological analysis of network attack vulnerability , 2006, PST.

[13]  Nick Feamster,et al.  Practical verification techniques for wide-area routing , 2004, Comput. Commun. Rev..

[14]  Ian F. Akyildiz,et al.  Wireless mesh networks: a survey , 2005, Comput. Networks.

[15]  A. Monticelli,et al.  Electric power system state estimation , 2000, Proceedings of the IEEE.

[16]  David R. Cheriton,et al.  Feedback based routing , 2003, CCRV.

[17]  Bruno Sinopoli,et al.  False Data Injection Attacks in Electricity Markets , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[18]  Randy L. Ekl,et al.  Security Technology for Smart Grid Networks , 2010, IEEE Transactions on Smart Grid.

[19]  Gene H. Golub,et al.  Matrix computations , 1983 .

[20]  Parv Venkitasubramaniam,et al.  Achievable Privacy in Aggregate Residential Energy Management Systems , 2015 .