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Wang, Chenguang, Tindemans, Simon, Pan, Kaikai, Palensky, Peter.  2020.  Detection of False Data Injection Attacks Using the Autoencoder Approach. 2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). :1—6.
State estimation is of considerable significance for the power system operation and control. However, well-designed false data injection attacks can utilize blind spots in conventional residual-based bad data detection methods to manipulate measurements in a coordinated manner and thus affect the secure operation and economic dispatch of grids. In this paper, we propose a detection approach based on an autoencoder neural network. By training the network on the dependencies intrinsic in `normal' operation data, it effectively overcomes the challenge of unbalanced training data that is inherent in power system attack detection. To evaluate the detection performance of the proposed mechanism, we conduct a series of experiments on the IEEE 118-bus power system. The experiments demonstrate that the proposed autoencoder detector displays robust detection performance under a variety of attack scenarios.
Hongbin, Z., Wei, W., Wengdong, S..  2020.  Safety and Damage Assessment Method of Transmission Line Tower in Goaf Based on Artificial Intelligence. 2020 IEEE/IAS Industrial and Commercial Power System Asia (I CPS Asia). :1474—1479.
The transmission line tower is affected by the surface subsidence in the mined out area of coal mine, which will appear the phenomenon of subsidence, inclination and even tower collapse, threatening the operation safety of the transmission line tower in the mined out area. Therefore, a Safety and Damage Assessment Method of Transmission Line Tower in Goaf Based on Artificial Intelligence is proposed. Firstly, the geometric model of the coal seam in the goaf and the structural reliability model of the transmission line tower are constructed to evaluate the safety. Then, the random forest algorithm in artificial intelligence is used to evaluate the damage of the tower, so as to take protective measures in time. Finally, a finite element simulation model of tower foundation interaction is built, and its safety (force) and damage identification are experimentally analyzed. The results show that the proposed method can ensure high accuracy of damage assessment and reliable judgment of transmission line tower safety within the allowable error.
Song, Yufei, Yu, Zongchao, Liu, Xuan, Tian, Jianwei, CHEN, Mu.  2019.  Isolation Forest based Detection for False Data Attacks in Power Systems. 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia). :4170—4174.
Power systems become a primary target of cyber attacks because of the vulnerability of the integrated communication networks. An attacker is able to manipulate the integrity of real-time data by maliciously modifying the readings of meters transmitted to the control center. Moreover, it is demonstrated that such attack can escape the bad data detection in state estimation if the topology and network information of the entire power grid is known to the attacker. In this paper, we propose an isolation forest (IF) based detection algorithm as a countermeasure against false data attack (FDA). This method requires no tedious pre-training procedure to obtain the labels of outliers. In addition, comparing with other algorithms, the IF based detection method can find the outliers quickly. The performance of the proposed detection method is verified using the simulation results on the IEEE 118-bus system.
Khezrimotlagh, Darius, Khazaei, Javad, Asrari, Arash.  2019.  MILP Modeling of Targeted False Load Data Injection Cyberattacks to Overflow Transmission Lines in Smart Grids. 2019 North American Power Symposium (NAPS). :1—7.
Cyber attacks on transmission lines are one of the main challenges in security of smart grids. These targeted attacks, if not detected, might cause cascading problems in power systems. This paper proposes a bi-level mixed integer linear programming (MILP) optimization model for false data injection on targeted buses in a power system to overflow targeted transmission lines. The upper level optimization problem outputs the optimized false data injections on targeted load buses to overflow a targeted transmission line without violating bad data detection constraints. The lower level problem integrates the false data injections into the optimal power flow problem without violating the optimal power flow constraints. A few case studies are designed to validate the proposed attack model on IEEE 118-bus power system.
Wang, Yufeng, Shi, Wanjiao, Jin, Qun, Ma, Jianhua.  2019.  An Accurate False Data Detection in Smart Grid Based on Residual Recurrent Neural Network and Adaptive threshold. 2019 IEEE International Conference on Energy Internet (ICEI). :499—504.
Smart grids are vulnerable to cyber-attacks, which can cause significant damage and huge economic losses. Generally, state estimation (SE) is used to observe the operation of the grid. State estimation of the grid is vulnerable to false data injection attack (FDIA), so diagnosing this type of malicious attack has a major impact on ensuring reliable operation of the power system. In this paper, we present an effective FDIA detection method based on residual recurrent neural network (R2N2) prediction model and adaptive judgment threshold. Specifically, considering the data contains both linear and nonlinear components, the R2N2 model divides the prediction process into two parts: the first part uses the linear model to fit the state data; the second part predicts the nonlinearity of the residuals of the linear prediction model. The adaptive judgment threshold is inferred through fitting the Weibull distribution with the sum of squared errors between the predicted values and observed values. The thorough simulation results demonstrate that our scheme performs better than other prediction based FDIA detection schemes.
Ameli, Amir, Hooshyar, Ali, El-Saadany, Ehab F..  2019.  Development of a Cyber-Resilient Line Current Differential Relay. IEEE Transactions on Industrial Informatics. 15:305—318.
The application of line current differential relays (LCDRs) to protect transmission lines has recently proliferated. However, the reliance of LCDRs on digital communication channels has raised growing cyber-security concerns. This paper investigates the impacts of false data injection attacks (FDIAs) on the performance of LCDRs. It also develops coordinated attacks that involve multiple components, including LCDRs, and can cause false line tripping. Additionally, this paper proposes a technique for detecting FDIAs against LCDRs and differentiating them from actual faults in two-terminal lines. In this method, when an LCDR detects a fault, instead of immediately tripping the line, it calculates and measures the superimposed voltage at its local terminal, using the proposed positive-sequence (PS) and negative-sequence (NS) submodules. To calculate this voltage, the LCDR models the protected line in detail and replaces the rest of the system with a Thevenin equivalent that produces accurate responses at the line terminals. Afterwards, remote current measurement is utilized by the PS and NS submodules to compute each sequence's superimposed voltage. A difference between the calculated and the measured superimposed voltages in any sequence reveals that the remote current measurements are not authentic. Thus, the LCDR's trip command is blocked. The effectiveness of the proposed method is corroborated using simulation results for the IEEE 39-bus test system. The performance of the proposed method is also tested using an OPAL real-time simulator.
Rumez, Marcel, Dürrwang, Jürgen, Brecht, Tim, Steinshorn, Timo, Neugebauer, Peter, Kriesten, Reiner, Sax, Eric.  2019.  CAN Radar: Sensing Physical Devices in CAN Networks based on Time Domain Reflectometry. 2019 IEEE Vehicular Networking Conference (VNC). :1–8.
The presence of security vulnerabilities in automotive networks has already been shown by various publications in recent years. Due to the specification of the Controller Area Network (CAN) as a broadcast medium without security mechanisms, attackers are able to read transmitted messages without being noticed and to inject malicious messages. In order to detect potential attackers within a network or software system as early as possible, Intrusion Detection Systems (IDSs) are prevalent. Many approaches for vehicles are based on techniques which are able to detect deviations from specified CAN network behaviour regarding protocol or payload properties. However, it is challenging to detect attackers who secretly connect to CAN networks and do not actively participate in bus traffic. In this paper, we present an approach that is capable of successfully detecting unknown CAN devices and determining the distance (cable length) between the attacker device and our sensing unit based on Time Domain Reflectometry (TDR) technique. We evaluated our approach on a real vehicle network.
Lakshminarayana, Subhash, Belmega, E. Veronica, Poor, H. Vincent.  2019.  Moving-Target Defense for Detecting Coordinated Cyber-Physical Attacks in Power Grids. 2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). :1–7.
This work proposes a moving target defense (MTD) strategy to detect coordinated cyber-physical attacks (CCPAs) against power grids. A CCPA consists of a physical attack, such as disconnecting a transmission line, followed by a coordinated cyber attack that injects false data into the sensor measurements to mask the effects of the physical attack. Such attacks can lead to undetectable line outages and cause significant damage to the grid. The main idea of the proposed approach is to invalidate the knowledge that the attackers use to mask the effects of the physical attack by actively perturbing the grid's transmission line reactances using distributed flexible AC transmission system (D-FACTS) devices. We identify the MTD design criteria in this context to thwart CCPAs. The proposed MTD design consists of two parts. First, we identify the subset of links for D-FACTS device deployment that enables the defender to detect CCPAs against any link in the system. Then, in order to minimize the defense cost during the system's operational time, we use a game-theoretic approach to identify the best subset of links (within the D-FACTS deployment set) to perturb which will provide adequate protection. Extensive simulations performed using the MATPOWER simulator on IEEE bus systems verify the effectiveness of our approach in detecting CCPAs and reducing the operator's defense cost.
Nasiruzzaman, A. B. M., Akter, M. N., Mahmud, M. A., Pota, H. R..  2018.  Network Theory Based Power Grid Criticality Assessment. 2018 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES). :1-5.

A process of critical transmission lines identification in presented here. The criticality is based on network flow, which is essential for power grid connectivity monitoring as well as vulnerability assessment. The proposed method can be utilized as a supplement of traditional situational awareness tool in the energy management system of the power grid control center. At first, a flow network is obtained from topological as well as functional features of the power grid. Then from the duality property of a linear programming problem, the maximum flow problem is converted to a minimum cut problem. Critical transmission lines are identified as a solution of the dual problem. An overall set of transmission lines are identified from the solution of the network flow problem. Simulation of standard IEEE test cases validates the application of the method in finding critical transmission lines of the power grid.

Liang, G., Weller, S. R., Zhao, J., Luo, F., Dong, Z. Y..  2017.  False Data Injection Attacks Targeting DC Model-Based State Estimation. 2017 IEEE Power Energy Society General Meeting. :1–5.

The false data injection attack (FDIA) is a form of cyber-attack capable of affecting the secure and economic operation of the smart grid. With DC model-based state estimation, this paper analyzes ways of constructing a successful attacking vector to fulfill specific targets, i.e., pre-specified state variable target and pre-specified meter target according to the adversary's willingness. The grid operator's historical reading experiences on meters are considered as a constraint for the adversary to avoid being detected. Also from the viewpoint of the adversary, we propose to take full advantage of the dual concept of the coefficients in the topology matrix to handle with the problem that the adversary has no access to some meters. Effectiveness of the proposed method is validated by numerical experiments on the IEEE-14 benchmark system.

Gavgani, M. H., Eftekharnejad, S..  2017.  A Graph Model for Enhancing Situational Awareness in Power Systems. 2017 19th International Conference on Intelligent System Application to Power Systems (ISAP). :1–6.

As societies are becoming more dependent on the power grids, the security issues and blackout threats are more emphasized. This paper proposes a new graph model for online visualization and assessment of power grid security. The proposed model integrates topology and power flow information to estimate and visualize interdependencies between the lines in the form of line dependency graph (LDG) and immediate threats graph (ITG). These models enable the system operator to predict the impact of line outage and identify the most vulnerable and critical links in the power system. Line Vulnerability Index (LVI) and Line Criticality Index (LCI) are introduced as two indices extracted from LDG to aid the operator in decision making and contingency selection. This package can be useful in enhancing situational awareness in power grid operation by visualization and estimation of system threats. The proposed approach is tested for security analysis of IEEE 30-bus and IEEE 118-bus systems and the results are discussed.

Pan, K., Teixeira, A. M. H., Cvetkovic, M., Palensky, P..  2016.  Combined data integrity and availability attacks on state estimation in cyber-physical power grids. 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm). :271–277.

This paper introduces combined data integrity and availability attacks to expand the attack scenarios against power system state estimation. The goal of the adversary, who uses the combined attack, is to perturb the state estimates while remaining hidden from the observer. We propose security metrics that quantify vulnerability of power grids to combined data attacks under single and multi-path routing communication models. In order to evaluate the proposed security metrics, we formulate them as mixed integer linear programming (MILP) problems. The relation between the security metrics of combined data attacks and pure data integrity attacks is analyzed, based on which we show that, when data availability and data integrity attacks have the same cost, the two metrics coincide. When data availability attacks have a lower cost than data integrity attacks, we show that a combined data attack could be executed with less attack resources compared to pure data integrity attacks. Furthermore, it is shown that combined data attacks would bypass integrity-focused mitigation schemes. These conclusions are supported by the results obtained on a power system model with and without a communication model with single or multi-path routing.

Kuntz, K., Smith, M., Wedeward, K., Collins, M..  2014.  Detecting, locating, amp; quantifying false data injections utilizing grid topology through optimized D-FACTS device placement. North American Power Symposium (NAPS), 2014. :1-6.

Power grids are monitored by gathering data through remote sensors and estimating the state of the grid. Bad data detection schemes detect and remove poor data. False data is a special type of data injection designed to evade typical bad data detection schemes and compromise state estimates, possibly leading to improper control of the grid. Topology perturbation is a situational awareness method that implements the use of distributed flexible AC transmission system devices to alter impedance on optimally chosen lines, updating the grid topology and exposing the presence of false data. The success of the topology perturbation for improving grid control and exposing false data in AC state estimation is demonstrated. A technique is developed for identifying the false data injection attack vector and quantifying the compromised measurements. The proposed method provides successful false data detection and identification in IEEE 14, 24, and 39-bus test systems using AC state estimation.

Marashi, K., Sarvestani, S.S..  2014.  Towards Comprehensive Modeling of Reliability for Smart Grids: Requirements and Challenges. High-Assurance Systems Engineering (HASE), 2014 IEEE 15th International Symposium on. :105-112.

Smart grids utilize computation and communication to improve the efficacy and dependability of power generation, transmission, and distribution. As such, they are among the most critical and complex cyber-physical systems. The success of smart grids in achieving their stated goals is yet to be rigorously proven. In this paper, our focus is on improvements (or lack thereof) in reliability. We discuss vulnerabilities in the smart grid and their potential impact on its reliability, both generally and for the specific example of the IEEE-14 bus system. We conclude the paper by presenting a preliminary Markov imbedded systems model for reliability of smart grids and describe how it can be evolved to capture the vulnerabilities discussed.