363 research outputs found

    Improving power theft detection using efficient clustering and ensemble classification

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    One of the main concerns of power generation systems around the world is power theft. This research proposes a framework that merges clustering and classification together in order to power theft detection. Due to the fact that most datasets do not have abnormal samples or are few, we have added abnormal samples to the original datasets using artificial attacks to create balance in the datasets and increase the correct detection rate. We improved the crow search algorithm (CSA) and used the weight feature of Crows to improve performance of clustering phase. Also, to create balance between diversification and intensification, we calculated the awareness probability parameter (AP) dynamically at iterations of the algorithm. To evaluate the performance, we used the cross validation technique have used the stacking technique in its training phase. The results of extensive experiments on three reference datasets showed high performance to detect power theft. The evaluation results showed that if the data is collected correctly and sufficiently, this framework can effectively detect power theft in any actual power grid. Also, for new attacks, if their patterns can be detected from the data, it is easily possible to implement these types of attacks

    Game-Theoretic and Machine-Learning Techniques for Cyber-Physical Security and Resilience in Smart Grid

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    The smart grid is the next-generation electrical infrastructure utilizing Information and Communication Technologies (ICTs), whose architecture is evolving from a utility-centric structure to a distributed Cyber-Physical System (CPS) integrated with a large-scale of renewable energy resources. However, meeting reliability objectives in the smart grid becomes increasingly challenging owing to the high penetration of renewable resources and changing weather conditions. Moreover, the cyber-physical attack targeted at the smart grid has become a major threat because millions of electronic devices interconnected via communication networks expose unprecedented vulnerabilities, thereby increasing the potential attack surface. This dissertation is aimed at developing novel game-theoretic and machine-learning techniques for addressing the reliability and security issues residing at multiple layers of the smart grid, including power distribution system reliability forecasting, risk assessment of cyber-physical attacks targeted at the grid, and cyber attack detection in the Advanced Metering Infrastructure (AMI) and renewable resources. This dissertation first comprehensively investigates the combined effect of various weather parameters on the reliability performance of the smart grid, and proposes a multilayer perceptron (MLP)-based framework to forecast the daily number of power interruptions in the distribution system using time series of common weather data. Regarding evaluating the risk of cyber-physical attacks faced by the smart grid, a stochastic budget allocation game is proposed to analyze the strategic interactions between a malicious attacker and the grid defender. A reinforcement learning algorithm is developed to enable the two players to reach a game equilibrium, where the optimal budget allocation strategies of the two players, in terms of attacking/protecting the critical elements of the grid, can be obtained. In addition, the risk of the cyber-physical attack can be derived based on the successful attack probability to various grid elements. Furthermore, this dissertation develops a multimodal data-driven framework for the cyber attack detection in the power distribution system integrated with renewable resources. This approach introduces the spare feature learning into an ensemble classifier for improving the detection efficiency, and implements the spatiotemporal correlation analysis for differentiating the attacked renewable energy measurements from fault scenarios. Numerical results based on the IEEE 34-bus system show that the proposed framework achieves the most accurate detection of cyber attacks reported in the literature. To address the electricity theft in the AMI, a Distributed Intelligent Framework for Electricity Theft Detection (DIFETD) is proposed, which is equipped with Benford’s analysis for initial diagnostics on large smart meter data. A Stackelberg game between utility and multiple electricity thieves is then formulated to model the electricity theft actions. Finally, a Likelihood Ratio Test (LRT) is utilized to detect potentially fraudulent meters

    Multi-Source Data Fusion for Cyberattack Detection in Power Systems

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    Cyberattacks can cause a severe impact on power systems unless detected early. However, accurate and timely detection in critical infrastructure systems presents challenges, e.g., due to zero-day vulnerability exploitations and the cyber-physical nature of the system coupled with the need for high reliability and resilience of the physical system. Conventional rule-based and anomaly-based intrusion detection system (IDS) tools are insufficient for detecting zero-day cyber intrusions in the industrial control system (ICS) networks. Hence, in this work, we show that fusing information from multiple data sources can help identify cyber-induced incidents and reduce false positives. Specifically, we present how to recognize and address the barriers that can prevent the accurate use of multiple data sources for fusion-based detection. We perform multi-source data fusion for training IDS in a cyber-physical power system testbed where we collect cyber and physical side data from multiple sensors emulating real-world data sources that would be found in a utility and synthesizes these into features for algorithms to detect intrusions. Results are presented using the proposed data fusion application to infer False Data and Command injection-based Man-in- The-Middle (MiTM) attacks. Post collection, the data fusion application uses time-synchronized merge and extracts features followed by pre-processing such as imputation and encoding before training supervised, semi-supervised, and unsupervised learning models to evaluate the performance of the IDS. A major finding is the improvement of detection accuracy by fusion of features from cyber, security, and physical domains. Additionally, we observed the co-training technique performs at par with supervised learning methods when fed with our features

    Data Challenges and Data Analytics Solutions for Power Systems

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    L'abstract è presente nell'allegato / the abstract is in the attachmen

    Deep Learning on Smart Meter Data: Non-Intrusive Load Monitoring and Stealthy Black-Box Attacks

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    Climate change and environmental concerns are instigating widespread changes in modern electricity sectors due to energy policy initiatives and advances in sustainable technologies. To raise awareness of sustainable energy usage and capitalize on advanced metering infrastructure (AMI), a novel deep learning non-intrusive load monitoring (NILM) model is proposed to disaggregate smart meter readings and identify the operation of individual appliances. This model can be used by Electric power utility (EPU) companies and third party entities, and then utilized to perform active or passive consumer power demand management. Although machine learning (ML) algorithms are powerful, these remain vulnerable to adversarial attacks. In this thesis, a novel stealthy black-box attack that targets NILM models is proposed. This work sheds light on both effectiveness and vulnerabilities of ML models in the smart grid context and provides valuable insights for maintaining security especially with increasing proliferation of artificial intelligence in the power system

    Energy Data Analytics for Smart Meter Data

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    The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal

    Recent Application in Biometrics

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    In the recent years, a number of recognition and authentication systems based on biometric measurements have been proposed. Algorithms and sensors have been developed to acquire and process many different biometric traits. Moreover, the biometric technology is being used in novel ways, with potential commercial and practical implications to our daily activities. The key objective of the book is to provide a collection of comprehensive references on some recent theoretical development as well as novel applications in biometrics. The topics covered in this book reflect well both aspects of development. They include biometric sample quality, privacy preserving and cancellable biometrics, contactless biometrics, novel and unconventional biometrics, and the technical challenges in implementing the technology in portable devices. The book consists of 15 chapters. It is divided into four sections, namely, biometric applications on mobile platforms, cancelable biometrics, biometric encryption, and other applications. The book was reviewed by editors Dr. Jucheng Yang and Dr. Norman Poh. We deeply appreciate the efforts of our guest editors: Dr. Girija Chetty, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park and Dr. Sook Yoon, as well as a number of anonymous reviewers

    Detecting Energy Theft and Anomalous Power Usage in Smart Meter Data

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    The success of renewable energy usage is fuelling the power grids most significant transformation seen in decades, from a centrally controlled electricity supply towards an intelligent, decentralized infrastructure. However, as power grid components become more connected, they also become more vulnerable to cyber attacks, fraud, and software failures. Many recent developments focus on cyber-physical security, such as physical tampering detection, as well as traditional information security solutions, such as encryption, which cannot cover the entire challenge of cyber threats, as digital electricity meters can be vulnerable to software flaws and hardware malfunctions. With the digitalization of electricity meters, many previously solved security problems, such as electricity theft, are reintroduced as IT related challenges which require modern detection schemes based on data analysis, machine learning and forecasting. The rapid advancements in statistical methods, akin to machine learning techniques, resulted in a boosted interest towards concepts to model, forecast or extract load information, as provided by a smart meter, and detect tampering early on. Anomaly Detection Systems discovers tampering methods by analysing statistical deviations from a defined normal behaviour and is commonly accepted as an appropriate technique to uncover yet unknown patterns of misuse. This work proposes anomaly detection approaches, using the power measurements, for the early detection of tampered with electricity meters. Algorithms based on time series prediction and probabilistic models with detection rates above 90% were implemented and evaluated using various parameters. The contributions include the assessment of different dimensions of available data, introduction of metrics and aggregation methods to optimize the detection of specific pattern, and examination of sophisticated threads such as mimicking behaviour. The work contributes to the understanding of significant characteristics and normal behaviour of electric load data as well as evidence for tampering and especially energy theft
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