Cybersecurity is a crucial aspect of microgrid systems, as they are vulnerable to various cyber threats that can compromise their functionality and safety. Cybersecurity of microgrids can be improved through the use of machine learning algorithms. These algorithms can detect and respond to potential cyber threats by analyzing patterns in network data, identifying anomalies, and taking appropriate measures to prevent or mitigate the consequences of a cyber-attack. ML makes the cybersecurity of the microgrid simple, effective, and cost-effective. There are mainly three steps followed by ML to detect an attack in the microgrid, data pre-processing, training process and detection process.
In recent years, the rapid growth in population is resulting in increased energy demand, leading to the depletion of fossil fuels. To meet the energy demand, it is necessary to integrate renewable energy sources into the grid. Renewable energy sources are a clean and carbon free alternative to fossil fuels. Nevertheless, these renewable energy sources are intermittent in nature, resulting in the generation-demand mismatch and increased frequency fluctuations. To meet the load demand in a short duration, it is necessary to integrate the energy storage devices into the grid. A distribution network operating in a controllable manner and consisting of distributed generating sources including renewable energy sources and controllable energy sources, energy storage devices and loads connected at different points is termed as microgrids. A microgrid can be operated in both grid connected mode and autonomous mode. In grid connected mode, the frequency dynamics are governed by the main grid. In autonomous mode, frequency dynamics depend on the microgrid. The frequency fluctuations in the islanded or autonomous mode are inevitable because of the intermittent nature of renewable energy sources and low system inertia. To mitigate the frequency fluctuations, it is necessary to integrate the control system with the microgrid.
Microgrid as Cyber Physical System
In a microgrid, distributed energy sources, energy storage devices, and loads are interconnected through power electronics converters. The physical components of the microgrid are interconnected through the information and communication system. The functionality of the communication systems is interfaced with the cyber systems. Hence, a microgrid can be considered a combination of the physical system and the cyber system. The physical system comprises of the system to be controlled, sensors and actuators. The cyber system comprises of control centers, computing centers and communication networks. The cyber-physical model of the microgrid consists of four layers: the physical power system layer, sensor and actuator layer, communication layer and management and control layer. The physical layer comprises the components of the microgrid such as the generator, transformer, power electronic converters and loads. The sensor and actuator layer consists of measurement and sensing devices to sense and measure the voltage, frequency, current and breaker status. The communication layer consists of switches, routers, and communication devices to exchange information between the physical system and the cyber system. The management and control layer is responsible for the operation of the microgrid. This layer receives the sensor and actuator data through the communication layer and generates control signals to optimize the performance of a microgrid.
Types of Attacks in Microgrid
Cyber-attacks
The communication channels in a cyber-physical system facilitate the information exchange between the physical layer and the cyber layer. Nevertheless, the communication channels are open and hence prone to the risks and threats of cyber-attacks. A cyber-attack in a microgrid refers to any malicious activity that aims to disrupt the normal operation of a microgrid system through the exploitation of vulnerabilities in the system's software, hardware, or communication networks. Cyber-attacks in microgrids can have significant impacts, such as loss of power, reduced reliability, and increased costs.
Availability Attack
The availability attacks are injected in the system to increase the traffic in the communication channels and make the information and resources unavailable. This type of attack is implemented by jamming the communication system, filling buffers or by altering the routing protocols. The result is that the microgrid's systems become unavailable to users, causing power outages and other disruptions.
Integrity Attack
Integrity attacks are inserted into the system to falsify the information and damage the stability and security of the system. This attack is performed on the system by manipulating or deleting the sensor and actuator measurements.
Confidentiality Attack
The confidentiality attack is defined as unauthorized access to a system via someone’s credentials with the intent of the malicious activity. This type of attack is performed on the system by injecting some data into the communication channels from where the attacker can capture confidential information. A confidential attack is considered to be a passive attack as the aim of this attack is to steal information rather than destroy the system.
The detection and mitigation of these attacks are characterized by human-centric approaches and non-human-centric approaches. Human-centric approaches include authentication, training, password, awareness, and updates. Non-human-centric approaches include blockchain, cloud computing, machine learning (ML), etc. Non-human centric approaches are efficient in detecting suspicious activities and mitigating digital threats. However, the implementation of these techniques across the microgrid is expensive. Cloud computing based security is efficient but requires high bandwidth. On the other hand, ML based attack detection and mitigation shows efficient performance, higher accuracy, the ability to adapt to dynamic data and is cost effective.
Machine Learning (ML) in the Cybersecurity of Microgrid
Cybersecurity is a crucial aspect of microgrid systems, as they are vulnerable to various cyber threats that can compromise their functionality and safety. Cybersecurity of microgrids can be improved through the use of machine learning algorithms. These algorithms can detect and respond to potential cyber threats by analyzing patterns in network data, identifying anomalies, and taking appropriate measures to prevent or mitigate the consequences of a cyber-attack. ML makes the cybersecurity of the microgrid simple, effective, and cost-effective. There are mainly three steps followed by ML to detect an attack in the microgrid, data pre-processing, training process and detection process.
Data Pre-processing
The data pre-processing in ML is performed using signal processing techniques and ML processing techniques. Signal processing is used when control signals are in the form of voltage, current and frequency. Signal processing techniques are effective in feature extraction and mitigating the curse of dimensionality. The signal processing techniques are implemented along with ML classifiers. Frequently used signal processing techniques are Hilbert Huang Transform (HHT), Fast Fourier Transform (FFT) and Variational Mode Decomposition (VDM) along with ML classifiers such as deep learning networks and Support Vector Machine (SVM). ML pre-processing techniques are implemented when data is captured from programmable logic controllers in the form of control protocols and address configuration features. ML pre-processing techniques are used for feature extraction and data compression. Commonly used ML pre-processing techniques are AutoEncoders (AE), Singular Value Decomposition (SVD) and Principal Component Analysis (PCA).
Training and Validation Process
The training of an ML attack detection model depends on the existence of a label, availability of data and data drift. The existence of the labels in training data is required to ensure whether supervised, unsupervised or reinforcement learning is used. In supervised learning, sampled labeled data is provided to train the attack model. In unsupervised learning data is not labeled, classified, or categorized and the algorithm acts on the data without any supervision. Reinforcement learning is a feedback based learning method in which the attack model learns from the feedback based on its previous responses and improves its performance. The learning type is decided on the basis of the existence of labels, data volume and class balancing. The training data is considered to be complete when all necessary patterns are available. If the data is complete and all the necessary patterns in the data are balanced, then a suitable ML technique can be implemented. If the data is incomplete, it is recommended to use data augmentation or domain adaptation to provide meaningful samples. Data complexity is analyzed using volume, velocity and variety termed 3V. The more 3V in data, the more complex the data is. The nature of the training data decides whether to use reinforcement learning or offline learning.
Detection Process
This process involves the detection of anomalies in the trained data. The detection process can be offline detection or in real-time detection. In offline detection, the trained model is tested by a set of training samples that are different from training and validation data sets. In real-time detection, the trained model is tested by sending real-time data of the microgrid. It is advantageous to use real-time detection to train the attack model to make the right threat mitigation decisions. The attack model decision is positive when an anomaly is detected in the system, and it is negative for the usual conditions.
Machine Learning (ML) based Intrusion Detection and Prevention
An intrusion detection system aims at auditing and analyzing security events to identify potentially malicious activities. The intrusion detection and prevention system (IDPS) acts as the line of defense by enhancing the encryption and authorization mechanism in a communication network. IDPS can be classified into four categories namely network-based IDPS (NIDPS), wireless IDPS (WIDPS), network behaviour analysis, and host-based IDPS (HIDPS). NIDPS monitors the total network traffic by analyzing the communication protocols of the system for malicious activity. WIDPS analyzes wireless networks for malicious traffic by monitoring the wireless network protocols. NBA examines network traffic to identify the cyber-attacks generating unusual traffic. HIDPS examines the data related to a single computing unit. With the advancement in technology, increased network traffic and multidimensional data, cyber-attacks are becoming sophisticated. Implementation of IDPS using conventional techniques is ineffective in dealing with growing security challenges. The implementation of ML techniques is effective in reducing the dimensions and classification tasks. The ML based IDPS learns from historic traffic data consisting of normal and anomalous traffic. ML-based IDPS reduces network traffic complexity to find correlations among data. It detects zero-day attacks and sophisticated attack patterns by learning from training samples and building the detection model. ML based IDPS can be used to deal with dynamic network traffic and continuous change in attack conditions. Commonly used ML algorithm for IDPS is the genetic algorithm, fuzzy logic, artificial neural networks and SVM.
Machine Learning (ML) Can Improve the Cybersecurity of Microgrids:
Anomaly Detection: ML algorithms can be used to detect abnormal patterns of behavior in microgrid systems, which can indicate the presence of a cyber-attack.
Intrusion Detection: ML algorithms can be used to identify malicious behavior and activities in microgrid systems and alert the system administrator to take appropriate actions.
Threat Analysis: ML algorithms can be used to analyze data from microgrid systems to identify potential cyber threats and provide recommendations on how to mitigate them.
Cyber Security Assessment: ML algorithms can be used to assess the cyber security risk of microgrid systems and provide recommendations on how to improve the overall security posture.
Access Control: ML algorithms can be used to enforce access control policies, allowing only authorized users to access microgrid systems and resources.
Approaches for Intrusion Detection and Prevention System (IDPS)
Intrusion detection and prevention are important components of the cyber security of microgrid systems, and machine learning can play a significant role in enhancing these processes. Here's how machine learning can be used for intrusion detection and prevention in microgrids:
Anomaly Detection: ML algorithms can be trained to detect abnormal patterns of behavior in microgrid systems, which can indicate the presence of a cyber-attack. This type of machine learning is often used in unsupervised learning algorithms, such as clustering or outlier detection.
Signature-based Detection: ML algorithms can be trained to recognize known cyber threats and anomalies based on patterns in data and behavior. This type of machine learning is often used in supervised learning algorithms, such as decision trees or support vector machines(SVM).
Behavior-based Detection: ML algorithms can be used to analyze the behavior of microgrid systems and identify potential cyber threats. This type of machine learning is often used in reinforcement learning algorithms, which allow the system to learn from its own experience and make decisions based on that information.
Real-time Intrusion Detection: ML algorithms can be used in real-time to detect and prevent cyber-attacks on microgrid systems as they occur. This type of machine learning is often used in deep learning algorithms, such as recurrent neural networks or convolutional neural networks.
Properties of IDPS in Microgrid
The aim of IDPS is to identify potential threats to the microgrid. Intrusion detection and prevention using ML in microgrids can have the following properties:
Real-time Analysis: ML algorithms can analyze data in real-time and detect anomalies or malicious behavior that indicate a cyber-attack. This enables prompt response to potential threats, reducing the risk of damage to the microgrid.
Scalability: ML algorithms can be scaled to accommodate large amounts of data and handle complex data structures. This is especially important in microgrid systems, which often involve a large number of interconnected devices and systems.
Automated Decision-making: ML algorithms can make decisions and take actions automatically, without human intervention. This can help reduce response times and increase the efficiency of the intrusion detection and prevention process.
Improved Accuracy: ML algorithms can learn from past experiences and improve the accuracy of intrusion detection and prevention over time. This can help reduce false positives and false negatives and improve the overall security posture of the microgrid.
Adaptability: ML algorithms can adapt to changing environments and new threats, improving their effectiveness over time.
Customizable: ML algorithms can be customized to fit the specific needs and requirements of a microgrid system.
Conclusion
The inclusion of communication systems in the microgrid facilitates information exchange. However, the increased application of communication channels provides adversaries a platform to jeopardize the security of the system. For the efficient and reliable operation of the microgrid, cyber-attack detection and mitigation is essential. With the invention of ML, it is easy to perform deep analysis, predict behaviour, detect, and mitigate attacks. ML aids in analyzing malicious behaviour including phishing attacks, confidentiality attacks, integrity attacks and availability attacks. With more advancements in the system, it is beneficial to utilize ML to secure the system from complicated situations and scenarios to meet the challenges of cyber security in the years to come. It's important to note that while ML can improve the accuracy and speed of intrusion detection and prevention in microgrids, it's only one aspect of a comprehensive cybersecurity strategy. Regular software updates, secure software development practices, and network segmentation are also essential for ensuring the security of microgrid systems.
Future Scope
With the increasing number of microgrids, the use of machine learning in cyber security is growing rapidly, as it offers the potential to quickly detect and respond to threats, helping to keep the critical infrastructure secure from cyber-attacks. Various companies are implementing ML to provide cyber security including Siemens Energy, ABB, BAE Systems, TCS, HCL Technologies and Infosys. Siemens Energy is implementing AI for securing the energy sector and it has invested €1,078 million in 2022 in R&D with an aim to protect business operations, information assets, data and information technology (IT) infrastructure. ABB Technology Ventures is the capital unit of ABB for industrial digitalization and has deployed $250 million since 2009 in startups including machine learning and cyber security. U.S. Defence Advanced Research Projects Agency (DARPA) has provided an $8.6 million contract to BAE Systems which is working on cyber security of the energy grid using ML to restore the power under cyber-attacks. This explains the rising popularity of ML and points to its increased use for safeguarding against malicious attacks.
