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

Abstract

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.

Learning-Based Non-Intrusive Electric Load Monitoring for Smart Energy Management

“…where m 2 (), m 3 () and m 4 () are 3×3, 5×5 and 7×7 convolutions, respectively. Third, it concatenates all inputs b i , i ∈ [1,4] and uses a 1×1 convolution to fuse all features from different scales. Finally, it employs attention mechanism to adaptively allocate weights to multi-scale features.…”

“…An effective solution to manage these facilities is smart city with Internet of Things (IoT), which is mostly benefitted from the recent development of Artificial Intelligence (AI) [1][2][3]. To support the smart city, an economic but efficient electric power management system is indispensable [4].…”

Research on Human Posture Estimation Algorithm Based on YOLO-Pose

“…Each feature point in the sample images captured by drones has 8 feature channels, which include parameters representing the detection box, confidence (conf), 2D screen coordinates (C1 and C2) of the key points for human pose estimation, and an identification indicator for the existence of key points (C3). The detection box has 4 parameters, namely the center point (bx, by), width (bw), and height (bh), where the center point of the detection box falls within the grid at the center of the feature map [26]. During the computation process, the center point coordinates of the detection box are first calculated, with gird i representing the i-th column and gird j representing the j-th row.…”

“…24, x FOR PEER REVIEW 11 of 21 the center point (bx, by), width (bw), and height (bh), where the center point of the detection box falls within the grid at the center of the feature map[26]. During the computation process, the center point coordinates of the detection box are first calculated, with girdi representing the i-th column and girdj representing the j-th row.…”

Data-Driven Approaches for Energy Theft Detection: A Comprehensive Review

“…Data-driven ETD has been proposed to detect, based on energy usage and power flow patterns, suspected energy theft. With the development of smart grids, the focus has shifted to data-based ETD because energy theft primarily occurs through data manipulation rather than the physical manipulation of meters [9].…”