Akıllı Şebekelerde İletişim Altyapısı ve Siber Güvenlik

GÜNDÜZ>, Muhammed Zekeriya; Daş, Resul

doi:10.21597/jist.655990

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…AMI helps this management by using control commands and real-time transmission of the data to utilities, customers, and third parties. Generally, an AMI system consists of smart meters, gateways, communication networks, and a headend system [ 5 ]. The most prominent component of AMI is smart meters.…”

Section: Introductionmentioning

confidence: 99%

Smart Grid Security: An Effective Hybrid CNN-Based Approach for Detecting Energy Theft Using Consumption Patterns

Gunduz,

Das

2024

Sensors

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In Internet of Things-based smart grids, smart meters record and report a massive number of power consumption data at certain intervals to the data center of the utility for load monitoring and energy management. Energy theft is a big problem for smart meters and causes non-technical losses. Energy theft attacks can be launched by malicious consumers by compromising the smart meters to report manipulated consumption data for less billing. It is a global issue causing technical and financial damage to governments and operators. Deep learning-based techniques can effectively identify consumers involved in energy theft through power consumption data. In this study, a hybrid convolutional neural network (CNN)-based energy-theft-detection system is proposed to detect data-tampering cyber-attack vectors. CNN is a commonly employed method that automates the extraction of features and the classification process. We employed CNN for feature extraction and traditional machine learning algorithms for classification. In this work, honest data were obtained from a real dataset. Six attack vectors causing data tampering were utilized. Tampered data were synthetically generated through these attack vectors. Six separate datasets were created for each attack vector to design a specialized detector tailored for that specific attack. Additionally, a dataset containing all attack vectors was also generated for the purpose of designing a general detector. Furthermore, the imbalanced dataset problem was addressed through the application of the generative adversarial network (GAN) method. GAN was chosen due to its ability to generate new data closely resembling real data, and its application in this field has not been extensively explored. The data generated with GAN ensured better training for the hybrid CNN-based detector on honest and malicious consumption patterns. Finally, the results indicate that the proposed general detector could classify both honest and malicious users with satisfactory accuracy.

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Section: Introductionmentioning

confidence: 99%

Smart Grid Security: An Effective Hybrid CNN-Based Approach for Detecting Energy Theft Using Consumption Patterns

Gunduz,

Das

2024

Sensors

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“…Industrial and social developments increase the need for energy. In order to come across the energy require, the existing nonrenewable energy sources are running out, and alternative energy sources and alternative technologies have started to be sought [1] It is known that in the recent past, Turkey's electricity energy transmission network has experienced overloading, serious and long term collapses as a result of faulty planning. Alike location and risks are still being valued.…”

Section: Introductionmentioning

confidence: 99%

A Short Review on the Optimization Methods Using for Distributed Generation Planning

2023

ijSmartGrid

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Industrial and social developments increase the need for energy. Due to the fact that fossil fuels will run out to meet energy needs, alternative technologies have been sought. Interest in renewable energy sources has increased. However, since renewable energy sources vary according to geographical conditions, their continuity must be ensured. Therefore, the focus has been on distributed generation systems. The components of distributed generation systems are divided into renewable, non-renewable and storage systems. Technical difficulties may arise in the connection of distributed generation systems with renewable resources. Therefore, it is very important to optimize distributed generation so that the distributed grid provides the expected power. Optimization systems are emphasized so as to decrease efficiency, reduce costs and cut down power fluctuations in distributed generation systems. In this study, distributed generation system, renewable energy source and energy storage system are mentioned. Metaheuristic methods for the efficiency of an energy system are studied. Evolution based algorithms can only keep the search space information in the iteration found, while swarm based algorithms can store the search space information throughout the iteration. Most metaheuristic algorithms use the biological cycle process, swarm behaviour, and physical laws. Evolution based and swarm-based optimization methods are examined in metaheuristic methods. As evolution based metaheuristic methods, genetic algorithm and swarm based optimization methods particle swarm optimization, ant colony optimization, grey wolf optimization, bat algorithm, whale optimization algorithm, cuckoo search algorithm are discussed. A general comparison of the investigated optimization methods is presented. It was concluded that the swarm optimization methods examined were able to make fast convergence and avoid local minima and computational efficiency evaluations.

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An effective DNN-based Approach for Detecting Energy Theft in Smart Grids through User Consumption Patterns

GÜNDÜZ,

DAŞ

2023

Türk Doğa Ve Fen Dergisi

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The advancement of the Internet has been progressively easing human life. The development of mobile communication technologies has led to the widespread adoption of Internet of Things (IoT) applications. Thus, most systems and devices have connected to the Internet more efficiently. The integration of communication systems into critical infrastructures, such as electricity grids, has given rise to the concept of IoT-based smart grids. In smart grid systems, data communication is facilitated through the Advanced Metering Infrastructure (AMI). Due to the inherent characteristics of communication systems, AMI may be vulnerable to cyber-attacks. Some vulnerabilities have resulted in the emergence of cyber-attack vectors against energy consumption data obtained from smart meters. In this study, an effective energy theft intrusion detection system (IDS) based on users' consumption patterns is proposed. A Deep Neural Network (DNN) based classification model was employed to assess the predictability of both honest and malicious consumption patterns. The proposed model exhibits high and adjustable performance. Extensive experiments have been carried out on a real consumption dataset of approximately 2000 customers. Manipulated data from real readings with two different attack vectors were injected into the dataset. K-fold cross-validation technique was used. The proposed model performed a high performance reaching up to 97.4% accuracy.

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Akıllı Şebekelerde İletişim Altyapısı ve Siber Güvenlik

Cited by 4 publications

References 27 publications

Smart Grid Security: An Effective Hybrid CNN-Based Approach for Detecting Energy Theft Using Consumption Patterns

Smart Grid Security: An Effective Hybrid CNN-Based Approach for Detecting Energy Theft Using Consumption Patterns

A Short Review on the Optimization Methods Using for Distributed Generation Planning

An effective DNN-based Approach for Detecting Energy Theft in Smart Grids through User Consumption Patterns

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