Exploring Malicious Meter Inspection in Neighborhood Area Smart Grids

In smart grids, smart meters may potentially be attacked or compromised to cause certain security risks. It is challenging to identify malicious meters when there are a large number of users. In this paper, we explore the malicious meter inspection (MMI) problem in neighborhood area smart grids. We propose a suite of inspection algorithms in a progressive manner. First, we present a basic scanning method, which takes linear time to accomplish inspection. The scanning method is efficient when the malicious meter ratio is high. Then, we propose a binary-tree-based inspection algorithm, which performs better than scanning when the malicious meter ratio is low. Finally, we employ an adaptive-tree-based algorithm, which leverages advantages of both the scanning and binary-tree inspections. Our approaches are tailored to fit both static and dynamic situations. The theoretical and experimental results have shown the effectiveness of the adaptive tree approach.

[1]  Yang Xiao,et al.  Cyber Security and Privacy Issues in Smart Grids , 2012, IEEE Communications Surveys & Tutorials.

[2]  B.F. Wollenberg,et al.  Toward a smart grid: power delivery for the 21st century , 2005, IEEE Power and Energy Magazine.

[3]  Lingfeng Wang,et al.  Support vector machine based data classification for detection of electricity theft , 2011, 2011 IEEE/PES Power Systems Conference and Exposition.

[4]  Jianqing Zhang,et al.  Application-Aware Secure Multicast for Power Grid Communications , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[5]  Dmitry Podkuiko,et al.  Energy Theft in the Advanced Metering Infrastructure , 2009, CRITIS.

[6]  Deepa Kundur,et al.  Towards modelling the impact of cyber attacks on a smart grid , 2011, Int. J. Secur. Networks.

[7]  Yang Xiao,et al.  A survey of communication/networking in Smart Grids , 2012, Future Gener. Comput. Syst..

[8]  Yang Xiao,et al.  Building Accountable Smart Grids in Neighborhood Area Networks , 2011, 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011.

[9]  D. Du,et al.  Combinatorial Group Testing and Its Applications , 1993 .

[10]  Bo Fu,et al.  SCADA communication and security issues , 2014, Secur. Commun. Networks.

[11]  A. Sterrett.  On the Detection of Defective Members of Large Populations , 1957 .

[12]  Thomas H. Cormen,et al.  Introduction to algorithms [2nd ed.] , 2001 .

[13]  R.E. Brown,et al.  Impact of Smart Grid on distribution system design , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[14]  H. Farhangi,et al.  The path of the smart grid , 2010, IEEE Power and Energy Magazine.

[15]  Xin-She Yang,et al.  Introduction to Algorithms , 2021, Nature-Inspired Optimization Algorithms.

[16]  Shu Dong XIAO.  Words from Editor‐in‐Chief of the Journal of Digestive Diseases , 2012 .

[17]  Yang Xiao,et al.  Non-repudiation in neighborhood area networks for smart grid , 2013, IEEE Communications Magazine.

[18]  Peng Liu,et al.  Secure and privacy-preserving information aggregation for smart grids , 2011, Int. J. Secur. Networks.

[19]  Yu Er-keng.  The vision of future smart grid , 2008 .

[20]  Shen Jie.  Survey on Smart Grid Technology , 2009 .

[21]  J.E.R. Alves,et al.  Identification of energy theft and tampered meters using a central observer meter: a mathematical approach , 2003, 2003 IEEE PES Transmission and Distribution Conference and Exposition (IEEE Cat. No.03CH37495).

[22]  Yang Xiao,et al.  Accountability in smart grids , 2011, 2011 IEEE Consumer Communications and Networking Conference (CCNC).

[23]  Jing Liu,et al.  Achieving Accountability in Smart Grid , 2014, IEEE Systems Journal.

[24]  Patrick D. McDaniel,et al.  Security and Privacy Challenges in the Smart Grid , 2009, IEEE Security & Privacy.

[25]  Georgios Kalogridis,et al.  Privacy protection system and metrics for hiding electrical events , 2011, Int. J. Secur. Networks.