Machine learning (ML) anomaly detectors are commonly used to identify cyber-attacks on smart power grids because they can detect new (i.e., zero-day) attacks by classifying deviations from normal patterns as anomalies. Deeplearning-based anomaly detectors offer superior performance but are highly sensitive to the selection of threshold values for defining anomalies. Conversely, traditional (or shallow-based) detectors avoid this threshold sensitivity but often underperform, particularly when dealing with complex interdependent data. Moreover, like all ML models, these detectors are vulnerable to adversarial evasion attacks, where adversaries make small and subtle manipulations to false data to evade detection. To address these issues, we propose a robust hybrid-based anomaly detector that combines the strengths of both deep and shallow-based and is trained using explanations derived from power consumption readings rather than the raw readings themselves. This hybrid approach not only mitigates threshold sensitivity and improves performance but also enhances robustness against white-box evasion attacks. Additionally, we introduce an interpretability method using occlusion sensitivity, which helps explain how a classification decision is made for an input power consumption sample, thereby increasing trust, reliability, and understanding of various attack patterns.

Existing literature confirms the ability of machine learning to identify fraudulent smart grid power consumers who report false consumption readings to pay less electricity bills. Additionally, federated learning (FL) shows promise as a way to train the detection model without requiring data sharing, thereby safeguarding consumer privacy. However, malicious participants (i.e., clients) in FL training can launch adversarial attacks by training their local models with specially crafted low-consumption data to inject a Trojan into the global model. This Trojan can then be activated during the evaluation phase to evade the detection of false data. To the best of our knowledge, not enough research has been done on this topic in the context of unsupervised learning. The absence of labels in unsupervised learning exacerbates the effectiveness of Trojan attacks and renders it more challenging to design robust defense mechanisms. In this article, we first investigate the vulnerability of one-class classifiers to Trojan attacks. Then, we propose two defense approaches named layerwise close-to-median (LWCM) and Machine Unlearning to counter this attack. In LWCM, by choosing a FL client whose last layer model parameters are near to the median of all clients’ last layer parameters to update the global model, we can identify and exclude malicious updates. The idea is that the last layer parameters of honest clients should be similar, whereas those from malicious clients are different. With the majority of clients being honest, the median values are closer to the parameters of these clients, facilitating the detection of malicious clients. In Machine Unlearning, we utilize gradient ascent-based techniques to adapt models by selectively removing attacker-related data points. This is possible because honest clients generate data resembling that of malicious clients and employ a dual-component loss function to maintain model proficiency in recognizing benign power consumption patterns while eliminating malicious patterns. To show the seriousness of Trojan attacks and the effectiveness of our countermeasures, many experiments have been carried out.

In smart grids, smart meters (SMs) transmit power consumption data to utilities for billing and energy management. However, compromised SMs can report low consumption to reduce electricity bills. Deep reinforcement learning (DRL) detectors have recently been proposed to detect these attacks due to their adaptability to new attacks and changes in power consumption patterns. This article explores backdoor attacks targeting DRL detectors during training, aiming to introduce a vulnerability in the detector. These attacks make the detector misclassify false low-consumption data when trigger samples are used while maintaining normal classification accuracy otherwise. We propose a DRL-based attack model that generates stealthy and unique trigger samples using cosine similarity. Our evaluations show the attack is initially highly successful, but its success diminishes with honest data used for incremental training of the detector. To sustain high success rates, attackers must influence incremental training. We also propose defenses, including data filtration during the preparation stage, adversarial training for the defense model during the training stage, and a combined approach, with experiments validating their effectiveness.

Federated Learning (FL) enables decentralized training of machine learning (ML) models, making it a valuable approach for detecting false data in smart power grids (SGs) to enhance grid stability while protecting consumers privacy. However, FL-based ML models remain vulnerable to adversarial attacks during both training and inference phases, which can compromise data security. To address these vulnerabilities, we first investigate the robustness of a novel FL-based false data detection approach using Explainable Artificial Intelligence (XAI), referred to as XAI-based FL detection. This approach utilizes explanations of consumers power consumption data, rather than raw data, during the training process. We assess the robustness of the XAI-based FL detection compared to traditional data-driven FL detection against two types of adversarial attacks: Gradient Inversion attacks in the training phase, where adversaries reconstruct private data from shared gradients, and Evasion attacks in the inference phase, where adversaries subtly modify input data to deceive the detection model. Then, we propose a secure XAI-based FL detector with adversarial training to defend against both attack types. The key idea is that XAI helps mask model gradients during training because XAI-generated explanations remain nearly identical across different samples. Therefore, attackers struggle to accurately reconstruct the original training data, even if they obtain precise explanations using gradient inversion attacks. Additionally, XAI effectively distinguishes between benign and malicious samples. When combined with adversarial training, XAI strengthens model robustness against evasion attacks without compromising accuracy, effectively resolving the trade-off between security and performance. Our proposed detector reduced the success rate of evasion attacks from 94.99% to 29.11 explanations, and further to 0% with adding adversarial training. It also increased the mean square error for gradient inversion attacks from 0.01 to 2.60 in the most severe attack scenarios, making such attacks ineffective.

In the advanced metering infrastructure (AMI) of the smart grid, smart meters (SMs) are deployed to collect fine-grained electricity consumption data, enabling billing, load monitoring, and efficient energy management. However, some consumers engage in fraudulent behavior by hacking their meters, leading to either traditional electricity theft or more sophisticated evasion attacks. Evasion attacks aim to illegally reduce electricity bills while deceiving theft detection mechanisms. The current methods for identifying such attacks raise privacy concerns due to the need for access to consumers' detailed consumption data to train detection mechanisms. To address privacy concerns, federated learning (FL) is proposed as a collaborative training approach across multiple consumers. Adversarial training (AT) has shown promise in countering evasion threats on machine learning models. This paper, first, investigates the susceptibility of traditional electricity theft classifiers trained by FL to evasion attacks for both independent and identically distributed (IID) and Non-IID consumption data. Then, it investigates the effectiveness of AT in securing the global electricity theft detector against evasion attacks, assuming no misbehavior from the participant consumers in the FL process. After that, we introduce a novel attack, called Distillation, which can be launched during the AT process to make the global model susceptible to evasion at inference time. Finally, extensive experiments are conducted to validate the severity of the proposed attack.

In smart power grids, electricity theft causes huge economic losses to electrical utility companies. Machine learning (ML), especially deep neural network (DNN) models hold state-of-the-art performance in detecting electricity theft cyberattacks. However, DNN models are vulnerable to adversarial attacks, i.e., evasion attacks. In this work, we study the vulnerability of the DNN-based electricity theft detectors against evasion attacks and the influence of the model's regularization (generalization) on robustness. We cluster the power consumers and train a detector for each cluster, and compare the performance and robustness of this detector to a global detector that is trained on all the consumers, data. The results indicate that the cluster-based detector is not only more robust against evasion attacks but also enhances normal classification accuracy because its training data has more consumption pattern similarity compared to the training data of the global detector which requires higher level of regularization. Moreover, unlike the existing solutions that sacrifice the normal accuracy of the model to improve the robustness against evasion attacks, the proposed cluster-based detector holds state-of-the-art performance in both robustness and accuracy.

The utilization of Advanced Metering Infrastructure (AMI) technology is for recording and billing customers for electricity consumption. This technology is vulnerable to cyber-attacks where customers under report their electricity usage, causing financial losses for electricity providers. Machine learning (ML) can be used to detect electricity theft, but it is challenging due to the absence of malicious data. To address this challenge, most of the existing works proposed specific attacks, however, these works are only effective on the proposed attacks and fail on new attacks. Some works proposed using anomaly detectors trained only on begin dataset. However, they rely on specific attacks to set classification thresholds, leading to failure in detecting zero-day attacks. Therefore, this paper proposes a one-class classification approach for electricity theft detection depending only on benign data and without assuming any attacks. First, the paper re-evaluates an existing detector that sets a reconstruction error threshold. Then, it proposes a detector combining decisions from three one-class ML models, including a one-class support vector machine (OC-SVM) trained on benign data, an OC-SVM trained on the bottleneck outputs of an autoencoder trained only on benign data, an OC-SVM trained on the mean squared errors of the reconstructed data of the autoencoder. The evaluation results confirm the superiority of the proposed detector over its individual components and the existing detectors.

In the realm of smart grids, smart meters can be hacked to report false data to lower the consumers’ electricity bills. While machine learning (ML) techniques have shown promise in detecting false data, they are also prone to adversarial attacks, such as evasion attacks. This article investigates the impact of gradient-ensemble-based evasion attacks on the smart grid ML-based false data detectors, focusing on the white-box threat model where attackers possess detailed knowledge of the defense mechanism. First, we examines the vulnerability of three detectors (consumer-based, cluster-based, and global) to gradient-based evasion attacks. The evaluation results show an inverse relationship between robustness of the detectors and regularization (i.e., generalization), where higher data set variability usually causes higher regularization. Notably, minimal regularization level is observed when electricity consumption patterns are close. Our findings also indicate that the consumer-based detector exhibits higher accuracy and robustness but remains susceptible to zero day attacks and demands substantial computational resources for training an ML model for each consumer. In contrast, the cluster-based detector improves accuracy and exhibits satisfactory robustness compared to the global detector. Subsequently, we proposes two parallel-ensemble approaches (stacking and voting) for the cluster-based false data detectors trained on the adversarial samples. The evaluation results demonstrate that integrating clustering, adversarial training, and ensemble methods, the proposed detector enhances robustness against gradient-ensemble-based evasion attacks while significantly boosting accuracy. This stands in contrast to benchmark defenses, which often face a tradeoff between accuracy and robustness, sacrificing accuracy to bolster resilience against evasion attacks.

Decision tree (DT) models are widely used in medical applications where the size of the data sets is usually small or medium. Moreover, DT ensemble models are preferred over single DT models because of their higher accuracy in spite of the need for more overhead due to using multiple trees. Several schemes have been proposed for privacy-preserving cloud-based medical diagnosis using ensemble models. However, these schemes suffer from several limitations. First, they suffer from high computation/communication overheads due to using inefficient public-key cryptosystems. Second, none of them can simultaneously protect the intellectual property of the model and preserve the privacy of the patients’ data and diagnosis results. Finally, they do not provide inherent access control for the outsourced model and micropayment, in which only the registered patients can use the model and pay for the service. In this article, we develop a lightweight and privacy-preserving cloud-based medical diagnosis scheme using ensemble models with high accuracy and acceptable overhead. Using our scheme, the model owner can control the patients who can use the model. Also, for each classification operation, patients must make a micro-payment to pay for the diagnosis service. Our analysis indicates that our scheme can protect the model’s intellectual property and diagnose diseases without leaking any sensitive information about the patients’ medical data and the diagnosis results. Our experimental results demonstrate that our scheme requires less communication/computation overhead compared to the existing schemes.

The smart grid advanced metering infrastructure (AMI) is vulnerable to electricity theft cyber-attacks in which malicious smart meters report low readings to reduce the consumers’ bills. To avoid this problem, several machine-learning-based detectors have been proposed to detect electricity theft. Most of these detectors are global in the sense that they are trained on different consumption levels, including low and high consumptions, to be used for all consumers. In this article, we introduce a novel type of evasion attacks against global detectors as follows. A malicious consumer who has high consumption level can send false readings for a low-consumption profile (that resembles the profiles the detector is trained on) to evade the detector, i.e., steal electricity without being detected. We first conduct experiments to prove that the existing global detectors are vulnerable to this new kind of evasion attacks. To launch this attack, we train a generative adversarial network (GAN) on a real data set to generate fake low-consumption readings that can evade the detector. The given results indicate that the success rate of the attack is between 82% and 97%. To thwart this attack, we divide the consumers into clusters of close electricity consumption levels and train one detector for each cluster. Therefore, if a malicious consumer in any cluster tries to imitate the consumption profiles of consumers in other clusters, he/she will be detected. On the other hand, it is not profitable to imitate the electricity consumption profiles of consumers in his/her cluster to evade detection. To prove the effectiveness of our countermeasure, extensive experiments are conducted and the results indicate that our countermeasure can successfully thwart the attack.

Energy forecasting is important because it enables infrastructure planning and power dispatching while reducing power outages and equipment failures. It is well-known that federated learning (FL) can be used to build a global energy predictor for smart grids without revealing the customers’ raw data to preserve privacy. However, it still reveals local models’ parameters during the training process, which may still leak customers’ data privacy. In addition, for the global model to converge, it requires multiple training rounds, which must be done in a communication-efficient way. Moreover, most existing works only focus on load forecasting while neglecting energy forecasting in net-metering systems. To address these limitations, in this article, we propose a privacy-preserving and communication-efficient FL-based energy predictor for net-metering systems. Based on a data set for real power consumption/generation readings, we first propose a multidata-source hybrid deep learning (DL)-based predictor to accurately predict future readings. Then, we repurpose an efficient inner-product functional encryption (IPFE) scheme for implementing secure data aggregation to preserve the customers’ privacy by encrypting their models’ parameters during the FL training. To address communication efficiency, we use a change and transmit (CAT) approach to update local model’s parameters, where only the parameters with sufficient changes are updated. Our extensive studies demonstrate that our approach accurately predicts future readings while providing privacy protection and high communication efficiency.

Contact tracing is a very effective way to control the COVID-19-like pandemics. It aims to identify individuals who closely contacted an infected person during the incubation period of the virus and notify them to quarantine. However, the existing systems suffer from privacy, security, and efficiency issues. To address these limitations, in this article, we propose an efficient and privacy-preserving Blockchain-based infection control system. Instead of depending on a single authority to run the system, a group of health authorities, that form a consortium Blockchain, run our system. Using Blockchain technology not only secures our system against single point of failure and denial of service attacks, but also brings transparency because all transactions can be validated by different parties. Although contact tracing is important, it is not enough to effectively control an infection. Thus, unlike most of the existing systems that focus only on contact tracing, our system consists of three integrated subsystems, including contact tracing, public places access control, and safe-places recommendation. The access control subsystem prevents infected people from visiting public places to prevent spreading the virus, and the recommendation subsystem categorizes zones based on the infection level so that people can avoid visiting contaminated zones. Our analysis demonstrates that our system is secure and preserves the privacy of the users against identification, social graph disclosure, and tracking attacks, while thwarting false reporting (or panic) attacks. Moreover, our extensive performance evaluations demonstrate the scalability of our system (which is desirable in pandemics) due to its low communication, computation, and storage overheads.

In the smart grid, malicious customers may compromise their smart meters (SMs) to report false readings to achieve financial gains illegally. This causes hefty financial losses to the utility and may degrade the grid performance because the reported readings are used for energy management. This article is the first work that investigates this problem in the net-metering system, in which one SM is used to report the difference between the power consumed and the power generated. First, we prepare a benign data set for the net-metering system by processing a real power consumption and generation data set. Then, we propose a new set of attacks tailored for the net-metering system to create a malicious data set. After that, we analyzed the data and found time correlations between the net meter readings and correlations between the readings and relevant data obtained from trustworthy sources, such as solar irradiance and temperature. Based on the data analysis, we propose a general multidata-source deep hybrid learning-based detector to identify the false-reading attacks. Our detector is trained on net meter readings of all customers besides data from trustworthy sources to enhance the detector performance by learning the correlations between them. The rationale here is that although an attacker can report false readings, he cannot manipulate the solar irradiance and temperature values because they are beyond his control. Extensive experiments have been conducted, and the results indicate that our detector can identify the false-reading attacks with a high detection rate of 98.59% and a low false alarm of 2.92%.

Ride-sharing is a service that enables drivers to share trips with riders, which leads to several benefits such as sharing the travel cost and reducing traffic congestion. However, most of the existing ride-sharing systems rely on a central trusted unit to organize the service, which makes them subject to a single point of failure and attack, and lack of transparency. A few works have investigated decentralized ride-sharing systems, but they either do not consider privacy preservation or suffer from a tradeoff between privacy protection and accuracy due to using location cloaking technique. This paper proposes a Blockchain-based ride sharing organization system with accurate matching and privacy preservation. To achieve the accurate matching, instead of representing the ride-sharing area by a single grid, it is represented by several overlapping grids so that only near drivers/riders share rides. To preserve privacy, drivers/riders encrypt their offers/requests using a lightweight cryptosystem, and the Blockchain matches the encrypted offers and requests without being able to decrypt them. Our security and privacy analysis demonstrate that our system can organize the ride-sharing service in a secure and transparent way, and also preserve the privacy of drivers and riders. To evaluate the performance of our system, we have implemented it, and our measurements indicate that our system requires low communication and computation overheads.

There are different metering systems adopted in the advanced metering infrastructure (AMI) of the smart grid. Among these systems, the net-metering is a promising system that motivates customers to install renewable resources at their premises to generate electricity and sell it to the utility. In this system, the customer’s home is equipped with one smart meter to report the net readings representing the difference between the power consumed from the power grid and the power injected into the grid. However, malicious customers may compromise their meters to report false readings to the utility to illegally achieve financial gains. This not only causes huge losses to the utility, but also deteriorates the grid performance. To the best of our knowledge, this problem has not been investigated. Therefore, in this paper, we investigate the detection of false-reading attacks in the net-metering system for the first time. Specifically, we propose four sophisticated attacks customized for the net-metering system and use them to create a dataset containing both benign and malicious samples. We have analyzed the dataset and detected time correlations between the readings within the benign samples. Based on the data analysis, we propose a general deep-learning-based detector with hybrid architecture involving convolutional neural network (CNN) and gated recurrent unit neural network (GRU). We have evaluated our detector, and the results demonstrate that the detector can detect the false-reading attacks with high precision and recall, and low false alarm.

Reporting fine-grained power consumption readings periodically in advanced metering infrastructure (AMI) results in transmitting a massive amount of data by each smart meter (SM). To collect these readings efficiently, change and transmit (CAT) approach can be used. In CAT, the SM sends a consumption reading only when there is enough change in the consumption, which reduces the number of transmitted readings. However, using the CAT approach may trigger attackers to launch a presence-privacy attack (PPA) to infer sensitive information such as the absence of the house occupants by analyzing their SM’s transmission pattern. Therefore, in this paper, we propose a scheme, called “STID”, for collecting the power consumption readings efficiently in AMI networks while preserving the consumers’ privacy by transmitting spoofing transmissions based on an interactive deep-learning defense model. First, we create a dataset that contains the CAT transmission patterns using real power consumption readings and a clustering technique. Next, we train a deep-learning-based attacker model to launch PPA, and the results indicate that the success rate of the attacker is about 90%. Finally, to mitigate the PPA, we train a defense model using deep-learning to transmit spoofing transmissions. The evaluations of our envisioned STID scheme demonstrate a significant reduction in the attacker’s success rate while achieving high efficiency in terms of the number of readings that should be transmitted. Our measurements indicate that our proposed STID can reduce the attacker’s success rate to 6.12% and increase efficiency by about 38% compared to transmitting readings periodically.

Preserving the customers' privacy, while collecting their power consumption for monitoring and billing, is a prime concern in an Advanced Metering Infrastructure (AMI) network of the Smart Grid (SG). In this paper, we address this concern by formally formulating the data aggregation privacy problem, and propose a uniquely crafted Privacy-Preserving Monitoring and Billing scheme using Functional Encryption, referred to as PMBFE. Our proposed PMBFE fulfills four key objectives: (i) data aggregation for billing, (ii) dynamic pricing flexibility, (iii) load monitoring with customers' privacy preservation; and (iv) analysis on how the adopted functional encryption is able to jointly perform data aggregation efficiently and guarantee privacy-preservation. Our envisioned PMBFE approach is evaluated with extensive computer-based simulations. In contrast with the widely employed homomorphic-based encryption in AMI networks, our proposed PMBFE demonstrates significant performance improvement in terms of both communication and computation overheads while guaranteeing user-data privacy. Furthermore, the conducted security analysis exhibits the robustness of our proposal against collusion and eavesdropping attacks.