We Can Pay Less : Coordinated False Data Injection Attack Against Residential Demand Response in Smart Grids

被引:5
|
作者
Dayaratne, Thusitha [1 ]
Rudolph, Carsten [1 ]
Liebman, Ariel [1 ]
Salehi, Mahsa [1 ]
机构
[1] Monash Univ, Clayton, Vic, Australia
关键词
Demand response; False data injection attack; Impact alleviation; Smart grid vulnerabilities; ELECTRICITY MARKETS; INTEGRITY ATTACKS; TIME; IMPACT;
D O I
10.1145/3422337.3447826
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Advanced metering infrastructure, along with home automation processes, is enabling more efficient and effective demand-side management opportunities for both consumers and utility companies. However, tight cyber-physical integration also enables novel attack vectors for false data injection attacks (FDIA) as home automation/home energy management systems reside outside the utilities' control perimeter. Authentic users themselves can manipulate these systems without causing significant security breaches compared to traditional FDIAs. This work depicts a novel FDIA that exploits one of the commonly utilised distributed device scheduling architectures. We evaluate the attack impact using a realistic dataset to demonstrate that adversaries gain significant benefits, independently from the actual algorithm used for optimisation, as long as they have control over a sufficient amount of demand. Compared to traditional FDIAs, reliable security mechanisms such as proper authentication, security protocols, security controls or, sealed/controlled devices cannot prevent this new type of FDIA. Thus, we propose a set of possible impact alleviation solutions to thwart this type of attack.
引用
收藏
页码:41 / 52
页数:12
相关论文
共 50 条
  • [1] False Data Injection Attack Detection for Secure Distributed Demand Response in Smart Grids
    Dayaratne, Thusitha
    Salehi, Mahsa
    Rudolph, Carsten
    Liebman, Ariel
    [J]. 2022 52ND ANNUAL IEEE/IFIP INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS AND NETWORKS (DSN 2022), 2022, : 367 - 380
  • [2] Inherent Vulnerability of Demand Response Optimisation against False Data Injection Attacks in Smart Grids
    Dayaratne, Thusitha
    Rudolph, Carsten
    Liebman, Ariel
    Salehi, Mahsa
    [J]. NOMS 2020 - PROCEEDINGS OF THE 2020 IEEE/IFIP NETWORK OPERATIONS AND MANAGEMENT SYMPOSIUM 2020: MANAGEMENT IN THE AGE OF SOFTWARIZATION AND ARTIFICIAL INTELLIGENCE, 2020,
  • [3] On False Data Injection Attack against Dynamic State Estimation on Smart Power Grids
    Karimipour, Hadis
    Dinavahi, Venkata
    [J]. 2017 5TH IEEE INTERNATIONAL CONFERENCE ON SMART ENERGY GRID ENGINEERING (SEGE), 2017, : 388 - 393
  • [4] A novel detection and defense mechanism against false data injection attack in smart grids
    Cui, Jinlong
    Gao, Beibei
    Guo, Baojun
    [J]. IET GENERATION TRANSMISSION & DISTRIBUTION, 2023, 17 (20) : 4514 - 4524
  • [5] An Efficient Data-Driven False Data Injection Attack in Smart Grids
    Wen, Fuxi
    Liu, Wei
    [J]. 2018 IEEE 23RD INTERNATIONAL CONFERENCE ON DIGITAL SIGNAL PROCESSING (DSP), 2018,
  • [6] Robust Demand Response for Device Scheduling under False Data Injection Attacks in Smart Grids
    Dayaratne, Thusitha
    Rudolph, Carsten
    Liebman, Ariel
    Salehi, Mahsa
    [J]. 2020 IEEE PES INNOVATIVE SMART GRID TECHNOLOGIES EUROPE (ISGT-EUROPE 2020): SMART GRIDS: KEY ENABLERS OF A GREEN POWER SYSTEM, 2020, : 294 - 298
  • [7] High Impact False Data Injection Attack against Real-time Pricing in Smart Grids
    Dayaratne, Thusitha
    Rudolph, Carsten
    Liebman, Ariel
    Salehi, Mahsa
    He, Shan
    [J]. PROCEEDINGS OF 2019 IEEE PES INNOVATIVE SMART GRID TECHNOLOGIES EUROPE (ISGT-EUROPE), 2019,
  • [8] Interval Observer-Based Detection and Localization Against False Data Injection Attack in Smart Grids
    Luo, Xiaoyuan
    Li, Yating
    Wang, Xinyu
    Guan, Xinping
    [J]. IEEE INTERNET OF THINGS JOURNAL, 2021, 8 (02): : 657 - 671
  • [9] LSTM-Based False Data Injection Attack Detection in Smart Grids
    Zhao, Yi
    Jia, Xian
    An, Dou
    Yang, Qingyu
    [J]. 2020 35TH YOUTH ACADEMIC ANNUAL CONFERENCE OF CHINESE ASSOCIATION OF AUTOMATION (YAC), 2020, : 638 - 644
  • [10] Detection of False Data Injection Attack in Smart Grids via Interval Observer
    Wang, Xinyu
    Luo, Xiaoyuan
    Zhang, Mingyue
    Jiang, Zhongping
    Guan, Xinping
    [J]. PROCEEDINGS OF THE 2019 31ST CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2019), 2019, : 3238 - 3243