Cybersecurity Enhancement of Smart Grid: Attacks, Methods, and Prospects
Abstract
:1. Introduction
1.1. Preceding Affined Review Papers
1.2. Necessity for an Up-To-Date Review
1.3. Review Methodology Brief Description
1.4. Formation of the Remaining Work
2. Cyberattacks in Smart Grid
2.1. False Data Injection Attacks
2.2. Denial of Service (DoS) Attacks
2.3. Spoofing Attacks
3. Research Directions
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
ICTs | Information and communication technologies |
FDIA | False data injection attack |
DoS | Denial of service |
IEDs | Intelligent electronic devices |
GPS | Global positioning system |
DDoS | Distributed denial of service |
CPPS | Cyber physical power system |
DLLD | Deep learning-based location detection |
CNN | Convolutional neural network |
DLAA | Dynamic load altering attack |
VMD | Variational mode decomposition |
LSTM | Long short term memory |
CUSUM | Cumulative sum |
GAN | Generative adversarial network |
CRC | Cognitive risk control |
ICMP | Internet control message protocol |
GNSS | Global navigation satellite system |
SCADA | Supervisory control and data acquisition |
PMU | Phasor measurement unit |
NNGSD | Neural network GPS spoofing detection |
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Ref. | Description |
---|---|
[28] | This paper reviewed the FDIA attacks and discussed the economic and physical impact of the successful FDIA in smart grids. It also presented the defense strategies against FDIAs. |
[29] | The authors analyzed the impacts of cyberattacks on interactive models of smart grids and presented corresponding solution approaches as graphic dimension, mechanism dimension, and probability dimension methods. |
[30] | The authors introduced a layered approach to evaluate the security risks of both cyber and physical power systems against integrity and denial of service attacks. |
[31] | This paper detected the FDIA impacts toward non-technical losses, state estimation, and load forecasting using machine learning methods. |
[32] | This paper summarized the modeling methods of smart grid systems. This paper also analyzed the impacts of cyberattacks on control and stability of power system, and types of cyberattacks from the perspectives of simulation, probability, topology, and mechanism. It also introduced a unified framework for modeling physical and cyber components. |
Ref. | Victim Device | Type of Attack | Solution Method | Description |
---|---|---|---|---|
[33] | State estimator | FDI-Power buses | Convolutional neural network | It captures the inconsistency and co-occurrence dependency in the power flow measurements due to the potential attacks and detects the exact locations of FDIA in real-time by concatenating convolutional neural network with a standard bad data detector. |
[34] | SCADA and PMU measurements | FDI-Power measurements | Short-term state forecasting-based method | Proposed detector addresses the shortcoming of previous detectors in terms of handling critical measurements using temporal correlation. |
[35] | Smart grid | FDI- Control and Dynamic load altering attack | Adaptive sliding mode controller | It presents and adaptive sliding mode controller to ensure the reliable operation of the power system under unknown attack by using the adaptive mechanism. |
[36] | SCADA system | FDI-power grid state transitions and worst case detection delays | Quickest intrusion detection algorithm and Dynamic state estimation algorithm | It estimates and tracks the time-varying and non-stationary power grid states using Rao-CUSUM detector. |
[37] | Power system state estimators | FDI-Power buses and Sensors | Online sequential extreme learning machine and variational mode decomposition | An effective FDIA detection method is presented with temporal correlation. |
[38] | State estimation system | FDI-Power measurement | Equivalent-current based measurement transformation method | A weighted residual method is presented to detect and identify the FDIAs. |
[39] | Communication System | FDI-Generation scheduling and power shedding | LSTM | Attacks are detected by analyzing the feature vectors that learn the temporal correlations of the feature vectors in time sequence. |
[40] | Power monitoring meters and State estimators | FDI-Power measurements | Generalized CUSUM algorithm | A distributed sequential detector is proposed which uses level-triggered sampling technique. |
[41] | State estimation system | FDI-Power Buses and measurements | Residual pre-whitening algorithm | Residual pre-whitening technique based on the CUSUM of the one-shot statistic is used to resolve real-time FDIA detection mechanisms. |
[42] | Power network and Social network | FDI-Load Measurement | LSTM | A power load forecasting model based on deep learning and statistical method is proposed which is able to mitigate FDIA. |
[44] | Generator bus | FDI- Generator frequency and switching attack | Optimal partial state feedback law | A scheme based on manipulating the subset of control signals and changing the locations of attack continually to degrade system performance at a minimum cost using convex relaxation and Pontryagin’s maximum principle. |
[46] | Power system state estimator | FDI-Power measurement | Generative Adversarial Network (GAN)-based data model | Novel smooth training technique for GAN is developed and an online adaptive window is explored to maintain the state estimation integrity in real-time. |
[47] | Smart grid | FDI-Power bus | Cognitive risk control | The entropic state is used to detect and bring FDI attacks under control using CRC with task-switch control. |
Ref. | Victim Device | Type of Attack | Solution Method | Description |
---|---|---|---|---|
[48] | Smart meter and electric appliances | DDoS | Gaussian process | Gaussian process is used to detect DDoS attack using mean and covariance functions of underlying system model to predict its abnormal mode of operation. |
[49] | Sensors | DoS | Authorization, redundancy, and real-time location-based methods | Attacks in different communication layers and their defense mechanisms are discussed and dropped data are recorded even outside the sensor network. |
[50] | Electric system devices | DDoS | Activity level, cooperation degree, and deployment location-based defense mechanisms | Taxonomies of DDoS attacks and their corresponding defense mechanism are briefed. |
[51] | Cloud assisted applications | DDoS | Port hopping spread spectrum | DDoS attacks are prevented with the help of open port switching over time in a pseudo-random manner. The proposed method is verified on the PlanetLab test-bed and Amazon’s EC2. |
[52] | Advanced Metering Infrastructure | DDoS | Honeypot game strategy | The propose method helps in better analysis of strategic interactions between defenders and attacks. Attack detection rate are considerably improved, which shows promising security enhancement of AMI networks. |
[53] | Wireless relay nodes | DoS | Reputation-based topology configuration method | Successful isolation of attacked cyber nodes are achieved and data are continuously transmitted at low latency. |
[54] | Smart meter | DoS and channel jamming attacks | Intelligent local controller switching with channel hopping | Sufficient readings from meters are continuously collected through various local controllers to estimate the states of a grid under considered attacks. Optimal placement strategy of local controllers is also provided to avoid jamming attacks. |
[55] | Smart appliances | DoS | Minimally invasive attack mitigation via detection isolation and localization | The proposed mitigation method is scalable and has capability of timely detection of DDoS attacks. |
[56] | Sensor and controllers | DoS | Parametric feedback linearization control | Time-delay tolerance of power system is enhanced using communication latency values between controllers and sensors. |
[57] | Distance relay | DDoS | Directional comparison unblocking scheme | Only permissive overreaching transfer trip protection is studied. DDoS attacks are avoided in power system protection relays to some extents only. |
[58] | Client nodes | DDoS and replay attacks | Multi-homing based enhanced packet diffusion mechanism | Secure end-to-end data delivery is ensured with light weight mechanism against DDoS and replay attacks. |
Ref. | Victim Device | Type of Attack | Solution Method | Description |
---|---|---|---|---|
[63] | Phasor measurement unit | GPS spoofing time stamp attack | multi-antenna based quickest detection | The probabilistic metric is used which takes information of the carrier signal to noise ratio from two receive antennas to conduct the quickest GPS spoofing detection. |
[64] | Phasor measurement unit | GPS spoofing attack | Weighted lest square state estimation | The detection method estimates the state variables such as nodal voltages in rectangular coordinates, generator rotor angles and its rotor speed, as well as the time-varying attacks. |
[65] | Phasor measurement unit | GPS spoofing phase angle attack | multilayer perceptron neural network | The proposed neural network detection method is able to diagnose the GPS spoofing attacks and determine their location as well. The learning process of neural network is executed only once. |
[66] | Phasor measurement unit | GPS spoofing time attack | Kalman filter-based dynamic fusion estimator | The proposed method uses a state-space model combined with the data of SCADA and PMU under dynamic system conditions. Proposed detection approach can detect multi-GPS spoofing attacks. |
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Inayat, U.; Zia, M.F.; Mahmood, S.; Berghout, T.; Benbouzid, M. Cybersecurity Enhancement of Smart Grid: Attacks, Methods, and Prospects. Electronics 2022, 11, 3854. https://doi.org/10.3390/electronics11233854
Inayat U, Zia MF, Mahmood S, Berghout T, Benbouzid M. Cybersecurity Enhancement of Smart Grid: Attacks, Methods, and Prospects. Electronics. 2022; 11(23):3854. https://doi.org/10.3390/electronics11233854
Chicago/Turabian StyleInayat, Usman, Muhammad Fahad Zia, Sajid Mahmood, Tarek Berghout, and Mohamed Benbouzid. 2022. "Cybersecurity Enhancement of Smart Grid: Attacks, Methods, and Prospects" Electronics 11, no. 23: 3854. https://doi.org/10.3390/electronics11233854
APA StyleInayat, U., Zia, M. F., Mahmood, S., Berghout, T., & Benbouzid, M. (2022). Cybersecurity Enhancement of Smart Grid: Attacks, Methods, and Prospects. Electronics, 11(23), 3854. https://doi.org/10.3390/electronics11233854