article

An integrated detection system against false data injection attacks in the Smart Grid

Authors:

David Griffith,

Sulabh Bhattarai,

Nada GolmieAuthors Info & Claims

Security and Communication Networks, Volume 8, Issue 2

Pages 91 - 109

https://doi.org/10.1002/sec.957

Published: 25 January 2015 Publication History

Abstract

The Smart Grid is a new type of power grid that will use advanced communication network technologies to support more efficient energy transmission and distribution. The grid infrastructure was designed for reliability; but security, especially against cyber threats, is also a critical need. In particular, an adversary can inject false data to disrupt system operation. In this paper, we develop a false data detection system that integrates two techniques that are tailored to the different attack types that we consider. We adopt anomaly-based detection to detect strong attacks that feature the injection of large amounts of spurious measurement data in a very short time. We integrate the anomaly detection mechanism with a watermarking-based detection scheme that prevents more stealthy attacks that involve subtle manipulation of the measurement data. We conduct a theoretical analysis to derive the closed-form formulae for the performance metrics that allow us to investigate the effectiveness of our proposed detection techniques. Our experimental data show that our integrated detection system can accurately detect both strong and stealthy attacks. Copyright © 2014 John Wiley & Sons, Ltd.

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Cited By

Sendhil RAmuthan A(2022)Verifiable quaternion fully homomorphic encryption scheme for mitigating false data injection attacks by privacy preservation in fog environmentJournal of Information Security and Applications10.1016/j.jisa.2022.10338371:COnline publication date: 1-Dec-2022
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Bhattacharjee SMadhavarapu PDas SCao JHo Au MLin ZYung M(2021)A Diversity Index based Scoring Framework for Identifying Smart Meters Launching Stealthy Data Falsification AttacksProceedings of the 2021 ACM Asia Conference on Computer and Communications Security10.1145/3433210.3437527(26-39)Online publication date: 24-May-2021
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Index Terms

An integrated detection system against false data injection attacks in the Smart Grid
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
2. Networks
  1. Network performance evaluation

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Security and Communication Networks

Security and Communication Networks Volume 8, Issue 2

January 2015

251 pages

ISSN:1939-0114

EISSN:1939-0122

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John Wiley & Sons, Inc.

United States

Publication History

Published: 25 January 2015

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Cited By

Sendhil RAmuthan A(2022)Verifiable quaternion fully homomorphic encryption scheme for mitigating false data injection attacks by privacy preservation in fog environmentJournal of Information Security and Applications10.1016/j.jisa.2022.10338371:COnline publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1016/j.jisa.2022.103383
Yuan BKamruzzaman MShan S(2021)Application of Motion Sensor Based on Neural Network in Basketball Technology and Physical Fitness Evaluation SystemWireless Communications & Mobile Computing10.1155/2021/55629542021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/5562954
Bhattacharjee SMadhavarapu PDas SCao JHo Au MLin ZYung M(2021)A Diversity Index based Scoring Framework for Identifying Smart Meters Launching Stealthy Data Falsification AttacksProceedings of the 2021 ACM Asia Conference on Computer and Communications Security10.1145/3433210.3437527(26-39)Online publication date: 24-May-2021
https://dl.acm.org/doi/10.1145/3433210.3437527
Bhattacharjee SMadhavarapu VSilvestri SDas S(2021)Attack Context Embedded Data Driven Trust Diagnostics in Smart Metering InfrastructureACM Transactions on Privacy and Security10.1145/342673924:2(1-36)Online publication date: 21-Jan-2021
https://dl.acm.org/doi/10.1145/3426739
Bhattacharjee SDas S(2021)Detection and Forensics against Stealthy Data Falsification in Smart Metering InfrastructureIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2018.288972918:1(356-371)Online publication date: 6-Jan-2021
https://dl.acm.org/doi/10.1109/TDSC.2018.2889729
Tian PChen ZYu WLiao W(2021)Towards asynchronous federated learning based threat detectionComputers and Security10.1016/j.cose.2021.102344108:COnline publication date: 1-Sep-2021
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(2018)A cloud/edge computing streaming system for network traffic monitoring and threat detectionInternational Journal of Security and Networks10.1504/IJSN.2018.1001431713:3(169-186)Online publication date: 1-Jan-2018
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Bhattacharjee SThakur ADas SKim JAhn GKim SKim YLopez JKim T(2018)Towards Fast and Semi-supervised Identification of Smart Meters Launching Data Falsification AttacksProceedings of the 2018 on Asia Conference on Computer and Communications Security10.1145/3196494.3196551(173-185)Online publication date: 29-May-2018
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Padin AKebede YMorgan MVorva DFozdar AKalvaitis RRemley NTout SKallitsis M(2017)Diagnosing False Data Injection Attacks in the Smart Grid: a Practical Framework for Home-area NetworksProceedings of the 1st EAI International Conference on Smart Grid Assisted Internet of Things10.4108/eai.7-8-2017.152988(44-53)Online publication date: 7-Aug-2017
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Mallapuram SYu WMoulema PGriffith DGolmie NLiang FGrygiel KCerny TKim SWang W(2017)An Integrated Simulation Study on Reliable and Effective Distributed Energy Resources in Smart GridProceedings of the International Conference on Research in Adaptive and Convergent Systems10.1145/3129676.3129684(140-145)Online publication date: 20-Sep-2017
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