Cyber-Physical Attacks Targeting Communication-Assisted Protection Schemes

With the growing adoption of electric vehicles (EVs) and advent of bidirectional chargers, EV aggregators, such as charging stations, will become a major player in electricity markets, providing voltage regulation (VR) or other services. We present a novel and practical VR scheme that takes advantage of the charging flexibility of EVs in charging stations that are connected to buses in a distribution grid. This VR scheme relies on real-time measurements, as well as estimates of the distribution system state and regulation capacity of each charging station. We then propose a novel false data injection attack (FDIA) against the VR capacity estimation process that exploits the uncertainty in EV mobility and network conditions. We show the attack vector with the largest expected adverse impact is the solution of a stochastic optimization problem, subject to a constraint that ensures it bypasses bad data detection. We determine this attack vector by solving a sequence of convex quadratically constrained linear programs. The case studies examined in a co-simulation platform, based on two standard test feeders, reveal the vulnerability of the VR capacity estimation process.

State estimation under false data injection attacks: Security analysis and system protection

Recent times have witnessed increasing utilization of wide area measurements to design the transmission line protection schemes as wide area measurements improve the reliability of protection methods. Usage of ICT tools for communicating sensor measurement in power networks demands immunity and resiliency of the associated protection scheme against false data injection attack (FDIA). Immunity against malicious manipulation of sensor information is attainable by securing the communication channels connecting the sensors through cryptographic protocols, and encryption. However, securing all the sensors and communication channels is economically unviable. A practical solution involves securing a reduced set of sensors without compromising fault detection accuracy. With the aim of developing a simple, economically viable and FDIA resilient scheme under the assumption that the adversary has complete knowledge of the system dynamics, the present work proposes a logical analysis of data (LAD) based fault detection scheme. The proposed scheme identifies the minimal set of sensors for FDIA resiliency and detects the state (faulty or healthy) of the power network relying on the measurements received from the ‘minimal sensor set’ only. Validation of the proposed protection scheme on IEEE 9-bus system reveals that in addition to being FDIA resilient, it is reliable and computationally efficient.

This paper focuses on the development of a protection scheme for a smart distribution system containing DERs (Distributed Energy Resources) and Solid State Transformers (SSTs). First, the paper describes the fault analysis that needs to be conducted on such systems. Then, a communication assisted protection scheme is proposed, in order to overcome the shortcomings observed in the conventional protection schemes.

Protection of microgrid has become challenging due to the hosting of various actors such as distributed generation, energy storage systems, information and communication technologies, etc. The main protection challenges in the microgrid are the bi-directional power flow, protection blinding, sympathetic tripping, change in short-circuit level due to different modes of operation, and limited fault current contribution by converter-interfaced sources. This paper presents a comprehensive review of the available microgrid protection schemes which are based on traditional protection principles and emerging techniques such as machine learning, data-mining, wavelet transform, etc. A categorical assessment of the reviewed protection schemes is also presented. The key findings of the paper suggest that the time-domain and communication-assisted protection schemes could be suitable solutions to address the identified protection challenges in the microgrid.

The dynamic behavior of microgrid compromises the security and selectivity in conventional protection schemes. Islanding mode of operation provides continuous power supply to customers. Mesh configuration of microgrid can further improves the reliability. However, protection of mesh microgrid is a challenging task especially the isolation of faulted part only with rest of the system in operation. This paper presents a communication assisted protection scheme using negative sequence based voltage and current information of a microgrid. Using the change in angle difference of negative sequence voltage and current from pre-fault to fault case, method identifies the fault is internal or external. A hardware setup is established in the laboratory and performance of the relay algorithm is tested. Arduino uno microcontroller board is used as relay processor and XBee module is used for wireless communication.

In this article, a new procedure is proposed on the basis of Hilbert-Huang Transform and deep learning for cyber-attacks detection in direct current (DC) micro-grids (MGs) as well as detection of the attacks in distributed generation (DG) units and its sensors. An advanced elective group deep learning method with Krill Herd Optimization (KHO) algorithm is proposed. At first, Hilbert-Huang Transform is used with the aim of extracting the signals feature and next these features are applied as the multiple deep input basis models are made with the aim of capturing automatically sentient traits from raw fluctuation signals. At third, to make sure the variety of the basis patterns, linear decoder, denoising autoencoder and sparse autoencoder are applied to make various deep autoencoders, respectively. Further, Bootstrap is applied with the aim of designing separate educational data subsets for any base model. Fourth, for implementing selective ensemble learning, a combination strategy of enhanced weighted voting (EWV) with class-particular thresholds is studied. Eventually, KHO algorithm is applied with the aim of adaptive selecting the optimal class-specific thresholds. In the offered tactic, firstly, a DC micro-grid is functioned and controlled with the lack of any false data injection attacks (FDIAs) to collect adequate information within the usual operation needed for the educating of deep learning networks. It is noteworthy that, in the procedure of datum production, load variable is also determined with the aim of having distinctive datasets for cyber-attack scenarios and load variables. Also, to provide more realistic method, the smart plug-in electric vehicle is also considered in the model. Outcomes of Simulation in various scenarios are applied with the aim of verifying the benefit of the offered procedure. The outcomes propose that the offered procedure is able to more accurate and robust know various type of false data injection attack over than 93.76% accuracy detection of true rate.

Measurement protection to prevent cyber–physical attacks against power system State Estimation

The penetration of information and communication technologies (ICT) and the Internet of Things (IoT) in the smart grid (SG) power network paradigm has widened the landscape of cyber threats aiming at disrupting the grid operations. The wider exposure of the communication infrastructure increases the vulnerability of smart grids towards cyber intrusions. The most common impactful intrusions (commonly referred to as false data injection attack (FDIA)) involve falsification of sensor measurement while it is being transmitted to the control centre. Among FDIAs, the load redistribution(LR) attack causes the maximum disruption on the grid operations without being detected at the control centre. During the LR attack, the intruder attempts to virtually manipulate the sensor information at different locations. Analyzing the cyber vulnerability of power networks demands identifying possible LR attack scenarios that disrupt the grid operations to the maximum extent. In this regard, the present work proposes monitoring metrics, Cyber Resiliency Index (CRI), Cyber Severity Index (CSI) and Cyber Market Index (CMI) to quantify the impact of measurement falsification through LR attack scheme, executed with minimum budget and resources. The attack design task has been framed as a multi-objective optimization based on the trade-off between the objectives of maximum disruption and minimum attack cost. A set of solutions (Pareto-front), each pertaining to a bus location for LR attack, is derived post-convergence of the optimization problem. The proposed metrics have been validated for different attack scenarios in a real-time environment.

The emergence of cyber‐physical attacks brings a key challenge to the existing system integrity protection schemes (SIPSs) of smart grids. As one of typical cyber‐physical attacks, false data injection attacks (FDIAs) can bypass the existing Kalman filter‐based ‐detector detection techniques in SIPSs. To improve the detection performance against the FDIAs in SIPSs, this study proposes an unknown input observer (UIO)‐based detection and isolation method. Taking the stealthy characteristics of FDIAs into account, this study presents a set of UIOs to detect the FDIA based on the internally physical dynamics. Furthermore, a UIO‐based detection and isolation algorithm against the FDIAs is proposed based on the feature of residuals generated by UIOs. To detect the cyber attacks quickly and avoid missing detection, an adaptive threshold is designed to replace the precomputed threshold by taking the model linearised error and disturbance into account. Finally, comprehensive simulation results on the proposed algorithm are carried out, and the effectiveness of improving the detection performance in SIPSs is verified.

With advanced communication technologies, cyber-physical systems such as networked industrial control systems can be monitored and controlled by a remote control center via communication networks. While lots of benefits can be achieved with such a configuration, it also brings the concern of cyber attacks to the industrial control systems, such as networked manipulators that are widely adopted in industrial automation. For such systems, a false data injection attack on a control-center-to-manipulator (CC-M) communication channel is undesirable, and has negative effects on the manufacture quality. In this paper, we propose a resilient remote kinematic control method for serial manipulators undergoing a false data injection attack by leveraging the kinematic model. Theoretical analysis shows that the proposed method can guarantee asymptotic convergence of the regulation error to zero in the presence of a type of false data injection attack. The efficacy of the proposed method is validated via simulations.

This article discusses the consensus problem of the discrete-time multiagent system (MAS) by event-triggered secure control in infinite time domain against complex cooperative attacks, which consists of false data injection attacks (FDIAs) and replay attacks (RAs). Due to the openness of the network, attackers attempt to destroy the consensus by damaging the integrity of the measured signal in the information transmission channel. A novel complex cooperative attacks model is first built. FDIA and RA, which appear randomly on the data communication channel from sensor to controller, are modeled in a unified framework through Bernoulli process. The state-dependent event-triggered control scheme (ETCS), which reduce the demand for network bandwidth, is adopted to update control signals. This article is to design an appropriate ETCS to make MAS reach consensus under FDIA and RA. The sufficient conditions are derived to ensure the event-triggered consensus and consensus with guaranteed position performance by using the stochastic analysis method. Finally, the simulation results show the feasibility of the proposed ETCS.

To enhance the reliability, resiliency and sustainability of a microgrid (MG) in the presence of several distributed energy resources (DERs) and loads, communication systems play a vital role, forming it into a cyber-physical system (CPS). However, integration of information and communication technology (ICT) in the MG architecture exposes MG to potential malicious cyber attacks as well. Similarly, electric vehicles (EVs) also come with a host of many cybersecurity risks. The integration of power, communication and transportation sectors empowers the intruders to exploit the vulnerability and disrupt the operation of multiple sectors. Testbed plays a vital role in the study of the threat landscape and the impact of cyber attacks (such as denial of service (DoS) attacks and false data injection attacks (FDIA)) in such cyber-physical infrastructure. This study would further aid to develop and verify the detection and mitigation schemes against cyber attacks.This work presents a detailed study on the development of real-time (RT) cosimulation testbed for inverter-based MG and RT testbed for EV-charging infrastructure. The MG testbed consists of OP5700 RT simulator to emulate cyber-physical layer through HYPERSIM software; and SEL-3530 Real-Time Automation Controller (RTAC) hardware configured with ACSELERATOR RTAC SEL-5033 software. A human-machine interface (HMI) is further developed in ACSELERATOR Diagram Builder SEL-5035 software for local/remote monitoring and control. Furthermore, communication protocols such as Modbus, sampled measured values (SMVs), generic object-oriented substation event (GOOSE) and distributed network protocol 3 (DNP3) on an Ethernet-based interface were established to link the cyber and physical layers. Furthermore, the testbed for EV-charging infrastructure consists of AC and DC emulators to emulate the respective charging station (AC/DC) and EV. It also constitutes various charging cables supported for different charging protocols such as combined charging system (CCS), GB/T and CHAdeMO. To monitor the message exchanges, charging discovery system (CDS) hardware along with its software is used. Few of the test cases have been presented to demonstrate the ability of these testbeds.

State estimation is a key element in today's power systems for reliable system operation and control. State estimation collects information from a large number of meter measurements and analyzes it in a centralized manner at the control center. Existing state estimation approaches were traditionally assumed to be able to tolerate and detect random bad measurements. They were, however, recently shown to be vulnerable to intentional false data injection attacks. This paper fully develops the concept of load redistribution (LR) attacks, a special type of false data injection attacks, and analyzes their damage to power system operation in different time steps with different attacking resource limitations. Based on damaging effect analysis, we differentiate two attacking goals from the adversary's perspective, i.e., immediate attacking goal and delayed attacking goal. For the immediate attacking goal, this paper identifies the most damaging LR attack through a max-min attacker-defender model. Then, the criterion of determining effective protection strategies is explained. The effectiveness of the proposed model is tested on a 14-bus system. To the author's best knowledge, this is the first work of its kind, which quantitatively analyzes the damage of the false data injection attacks to power system operation and security. Our analysis hence provides an in-depth insight on effective attack prevention with limited protection resource budget.

False data injection attacks (FDIA) on state estimators are a kind of imminent cyber-physical security issue. Fortunately, it has been proved that if a set of measurements is strategically selected and protected, no FDIA will remain undetectable. In this paper, the metric Return on Investment (ROI) is introduced to evaluate the overall returns of the alternative measurement protection schemes (MPS). By setting maximum total ROI as the optimization objective, the previously ignored cost-benefit issue is taken into account to derive a realistic MPS for power utilities. The optimization problem is transformed into the Steiner tree problem in graph theory, where a tree pruning based algorithm is used to reduce the computational complexity and find a quasi-optimal solution with acceptable approximations. The correctness and efficiency of the algorithm are verified by case studies.