Integrated smart grid systems security threat model

Suleiman, Husam; Alqassem, Israa; Diabat, Ali; Arnautovic, Edin; Svetinovic, Davor

doi:10.1016/j.is.2014.12.002

Cited by 43 publications

(12 citation statements)

References 31 publications

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“…Data integrity and operational attacks are the most frequent cyber-security threats in the DS-SE infrastructure [20]. Specifically, FDIs have been shown to be one of the most common type of cyber-attack to be impacting the DS-SE [17]- [19]. In this section, we explore the FDI problem in the DS.…”

Section: False Data Injection In Distribution Systemmentioning

confidence: 99%

“…In the synchronous measurement update scheme, the state estimator maintains the same measurement vector until all of measurements readings arrive for the new timeinstant, and then the state estimator simultaneously updates the measurement vector elements for all the meters based on the newly received measurements (at time step u+s in the example shown in figure 1(b)). By gaining access to the metering devices, control center or communication routes [17]- [19], attackers can manipulate data at time steps of their choice to build the time-variable FDI [20]. Figure 1(c) shows a timevariable attack vector a.…”

Section: B Time-variable Fdimentioning

confidence: 99%

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Integration of Failure Detector in Bias Filter for Estimation of False Data Injection Cyberattacks

Langeroudi

Abdelaziz

2021

IEEE Access

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In order to enable many of the required smart grid functionalities, distribution systems are becoming increasingly dependent on state estimators. Many cyber-attacks attempt false data injection (FDI) attacks on such state estimators. The majority of the existing literature deal with FDIs in distribution systems state estimation either by the analysis of the residual vector elements, or by the analysis of historical data. In this work, we adopt an alternative approach for the detection of FDIs in distribution system state estimation, wherein FDIs are modelled as measurement biases and a bias filter is employed for FDI detection. Additionally, in order to enable the detection of time-variable FDIs, a failure detector is integrated in the recursive formulation of the bias filter, which is based on the Kalman filter. The developed approach is accordingly capable of identifying time-varying FDIs, which can evade many of the existing FDI detection methods. Simulation case studies are performed on the IEEE 13-node and 123-node feeders with different FDIs and the performance of the proposed approach is analyzed. INDEX TERMS Distribution system online monitoring, false data injection, Kalman filter, state estimation NOMENCLATURE Acronyms CBF Constant bias filter DS Distribution system FDI False data injection KF Kalman filter mFDBF Modified failure-detector in bias filter PDF Probability distribution function rBDD Residual-based bad data detector SCADA Supervisory control and data acquisition SE State estimation WLS Weighted least squares Indices a, b, c Three phases (superscript) i, j, s DS node index (subscript) k Discrete time index (subscript) T Matrix transpose (superscript) Sets φ Set of nodes with power measurement ψ Set of nodes with voltage measurement Variables , Voltage magnitude and angle at node i phase p , Active and reactive power at node i phase p b Bias vector z Measurement vector x State vector ̂ State estimates at time k r Residual vector ω Measurement noise Functions and matrices h(x) Measurement function Hk Measurement function Jacobian G Estimation gain matrix R, Q Measurements and states covariance matrix

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Section: False Data Injection In Distribution Systemmentioning

confidence: 99%

Section: B Time-variable Fdimentioning

confidence: 99%

Integration of Failure Detector in Bias Filter for Estimation of False Data Injection Cyberattacks

Langeroudi

Abdelaziz

2021

IEEE Access

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“…To date, studies considering SG-specific ISTs have gradually increased in number. Suleiman et al [44] proposed an SG Systems Security Threat Model (SSTM) based on a SCADA and AMI-involved SG. Their SSTM for application to a Security Quality Requirements Engineering (SQUARE) method [45] identified 76 ISTs.…”

Section: Sg Information Security Threatsmentioning

confidence: 99%

Determining Information Security Threats for an IoT-Based Energy Internet by Adopting Software Engineering and Risk Management Approaches

Chen

Huang

2019

Inventions

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This paper introduces an information security threat modeling (ISTM) scheme, which leverages the strengths of software engineering and risk management approaches, called I-SERM. The proposed I-SERM scheme effectively and efficiently prioritizes information security threats for IT systems that utilize a large number of sensors, such as Internet of Things (IoT)-based energy systems. I-SERM operations include determining functional components, identifying associated threat types, analyzing threat items, and prioritizing key threats with the use of software engineering tools such as product flow diagrams, use case diagrams, and data flow diagrams. By simultaneously referring to a proposed STRIDE+p matrix and a defined threat breakdown structure with reference score (TBS+r) scheme, the I-SERM approach enables systematic ISTM. To demonstrate the usability of I-SERM, this study presents a practical case aimed at electricity load balancing on a smart grid. In brief, this study indicates a substantive research direction that combines the advantages of software engineering and risk management into a systematic ISTM process. In addition, the demonstration of I-SERM in practice provides a valuable and practical reference for I-SERM application, and contributes to research in the field of information security designs for IoT-based Energy Internet systems. Inventions 2019, 4, 53 2 of 22ISTs, and taking the considerable remedial actions required are important steps that should be taken based on both theoretical and pragmatic consideration.The IoT has become a mainstream of ICT-based technology enabling smarter applications in the fields of facility monitoring and process management (i.e., Industry 4.0), medical areas, etc. IoT technology also makes possible the construction of smart power systems with intelligent energy management applications. Pan et al. [1] designed and implemented an IoT framework for smart energy functions in buildings. Marinakis and Doukas [2] also emphasized the enhancement of IoT-enabled intelligent energy management in buildings. In addition, Ejaz et al. [3] discussed the issue of how to adopt IoT technology to provide efficient energy management in smart cities. Noor-A-Rahim et al. [4] proposed an IoT-based framework to provide reliable communications between renewable energy facilities, remote IoT components, and control centers. These articles indicate that complex IoT-supported systems like smart buildings or cities, with many factors affecting the total energy consumption in different energy application scenarios, have been the focus of much Energy Internet research.Electricity is undoubtedly an indispensable form of energy for modern society. With the trend of rapid urbanization, people have become highly dependent on their use of electricity. As a result, the stability and reliability of the power supply plays a critical role in the operation of modern power systems. In addition to relying on the effective operation of power equipment, an ICT-supported infrastructure platform capable of superviso...

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“…Countermeasures against infection through external hardware or removable media include instituting strict technical controls and policies regarding removable media and notebook computers used for external maintenance. Removable media can be monitored through; creation of security boundary for removable media, encrypting individual data, exclusivity in use of ICS network and barriers preventing connection of removable media to the network [13]. For notebook computers used for external maintenance, data should only be exchange through removable media, quarantine networks introduced to enable external access computers, scanning for vulnerabilities before connecting to the network and encryption of such equipment [10].…”

Section: Malware Infiltration Through External Hardware and Removablementioning

confidence: 99%

Current Computer Network Security Issues/Threats

Yassir¹,

Ismaeel²

2016

IJCA

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Computer network security has been a subject of concern for a long period. Many efforts have been made to address the existing and emerging threats such as viruses and Trojan among others without any significant success. Worse, new issues and threats have been emerging given that technology changes and becomes obsolete in a short while. In this regard, this research delves into the current network security issues in order to give future directions of research in a bid to help security technologists know where to focus their resources. This research examines the threats in terms of whether they are hardware or software issues. The need of this research is well articulated with the current security threats and countermeasures clearly outlined. The research found the following as the current major security threats: social engineering and phishing, malware infiltration through external hardware and removable media, malware infection through intranet and Internet, invasion through remote access, human sabotage or error, DDoS attacks, and cloud and extranet components being compromised.

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Integrated smart grid systems security threat model

Cited by 43 publications

References 31 publications

Integration of Failure Detector in Bias Filter for Estimation of False Data Injection Cyberattacks

Integration of Failure Detector in Bias Filter for Estimation of False Data Injection Cyberattacks

Determining Information Security Threats for an IoT-Based Energy Internet by Adopting Software Engineering and Risk Management Approaches

Current Computer Network Security Issues/Threats

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