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Fast Power Grid Partition for Voltage Control With Balanced-Depth-Based Community detection Algorithm

IEEE TRANSACTIONS ON POWER SYSTEMS 2022年第2期37卷 1612-1622页

作者： Yang, Yang Sun, Yichao Wang, Qi Liu, Fusuo Zhu, Ling Nanjing Normal Univ Sch Elect & Automat Engn Nanjing 210023 Peoples R China NARI Grp Corp Dept Elect Network Anal Nanjing 210097 Peoples R China

Network partition in complex power networks is essential for the var-voltage control. Traditional partition methods such as Ward method are applied in practical power networks, but they are unable to evaluate the quality of partition results. Moreover, they lack efficiency when dealing with large-scale networks. Since complex operation characteristics and topology have emerged in recent power systems, the power grid partition requires higher efficiency and quality. Therefore, this paper proposes a fast network partition method with a balanced-depth-based community detection algorithm. Its aim is to significantly improve the efficiency of partition while maintaining high quality of partition, with which the inter-zone coupling is minimized while the intra-zone coupling is maximized. In the meantime, a surrogate-optimization-based selection algorithm is proposed to select the zonal pilot bus, based on which the secondary voltage control method is used to evaluate the quality of partition. Results from four case studies conducted in various power networks with different sizes, as compared to other partition methods, validate the high efficiency and high quality of the proposed power grid partition approach.

关键词： detection algorithms Power grids Partitioning algorithms Voltage measurement Power measurement Sensitivity Sun Community detection algorithm electrical distance hierarchical clustering algorithm voltage control power network partition modularity pilot bus

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An Anchor-Free Lightweight Deep Convolutional Network for Vehicle detection in Aerial Images

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第12期23卷 24330-24342页

作者： Shen, Jiaquan Zhou, Wangcheng Liu, Ningzhong Sun, Han Li, Deguang Zhang, Yongxin Luoyang Normal Univ Sch Informat Sci Luoyang 471022 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China

Vehicle object detection in aerial scenes has important applications in both military and civilian fields. Recently, deep learning has shown clear advantages in object detection, and the detection performance has been continuously improved. However, these deep object detection algorithms rely on anchor-based approaches accompanied by complex convolutional operations. In this paper, we establish a lightweight aerial vehicle object detection algorithm based on the method of anchor-free. The anchor-free based object detection method effectively gets rid of the limitation of detection model capability by the size of fixed anchor box, which reduces the set of parameters and provides a more flexible solution space. In addition, the proposed lightweight object feature extraction network effectively reduces the computational cost of the model, while improving the feature extraction capability of small objects. Besides, we use channel stacking to improve the object feature extraction capability of the lightweight network, and introduce the attention mechanism in the detection model to improve the efficiency of resource utilization. We evaluate the proposed detection algorithm on both the public aerial dataset and our collected aerial dataset, and the results show that our algorithm has significant advantages over other detection algorithms in detection accuracy and efficiency. The proposed detection algorithm achieves 89.1% and 92.6% mAP on the Munich dataset and the created dataset, and the detection time for each image is 1.21s and 0.036s, respectively.

关键词： Feature extraction Object detection Computational modeling Convolution Training detection algorithms Predictive models Vehicle detection aerial image anchor-free lightweight convolution network

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Out-of-Distribution detection Based on Feature Fusion in Neural Network Classifier Pre-Trained by PEDCC-Loss

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IEEE ACCESS 2022年 10卷 66190-66197页

作者： Zhu, Qiuyu Zheng, Guohui Shen, Jiakang Wang, Rui Shanghai Univ Sch Commun & Informat Engn Shanghai 201900 Peoples R China

Out-of-distribution (OOD) detection is related to the security and stability of deep learning models deployed in the real world. The existing OOD detection algorithms based on the neural network normally use a single scoring function to detect out-of-distribution examples, which start from the posterior probability and do not fully utilize the information of the pre-trained model. In this paper, based on our previous PEDCC-based work, feature fusion is explored in OOD detection to take maximum advantage of the pre-trained classifier features. Our improved method adopts a two-stage training approach, in which multiple OOD detection features of the first-stage neural network classifier are extracted as the input of the second-stage training. In addition, we propose the stop-near-saturation method, which can help the OOD detection algorithm find optimal network parameters without accessing OOD data. Extensive experiments on several public datasets and classification networks show that compared with other existing methods, this method has better OOD detection performance, and maintains the low computational complexity of the original PEDCC-based method.

关键词： Feature extraction detection algorithms Neural networks Training Perturbation methods Deep learning Licenses Feature fusion in-distribution neural network out-of-distribution detection

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A Novel Hybrid Model Based on Sentiment Analysis Using Community Guided Link Prediction for Connecting People With Similar Interest

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IEEE ACCESS 2024年 12卷 139435-139455页

作者： Luthra, Snigdha Kumar, Rakesh Gupta, Meenu Chandigarh Univ Dept Comp Sci & Engn Mohali 140413 Punjab India

In this study, a novel hybrid recommendation model is designed for popular social media platforms such as Facebook, Twitter, WhatsApp, and Instagram. Recognizing the global significance of these platforms, the model aims to facilitate connections among users with similar interests, leveraging community detection algorithms. By integrating sentimental analysis, link prediction, and community detection techniques, a comprehensive network is constructed that enhances user experience and addresses the escalating issue of cyberbullying in the era of increasing connectivity. The proposed model strategically combines nodes and edges to form communities tailored to individual user interests. Social media comments, gathered randomly from the Facebook API, undergo sentimental analysis to evaluate the model's performance. The innovative Community-guided Link Prediction algorithm using Sentimental Analysis (CLPSA) for multilayer networks is employed, aiding in the identification of nodes associated with fake comments and thereby mitigating cyberbullying problems. Integrating sentiment analysis enhances understanding of human emotions, and users in the same cluster are more likely to connect in the future, promoting positive engagement. The model's novelty lies in its utilization of the Proposed Cluster Edge Betweenness algorithm, which identifies clusters of users with denser connections among them while being loosely connected to other separate modules. This approach helps create a network structure that fosters positive interactions and reduces the occurrence of intolerable comments. The experimental comparison of community detection algorithms underscores the effectiveness of our approach, particularly the superior performance of the Cluster Edge Betweenness algorithm. It achieves high accuracy in identifying user interests and forming cohesive clusters, essential for enhancing user engagement and community cohesion within social media networks. The qualitative analysis of c

关键词： Cyberbullying Prediction algorithms Clustering algorithms Blogs detection algorithms Analytical models Semantics Sentiment analysis Community detection social media networks (SMNs) sentimental analysis cyberbullying link prediction

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Pixel Bleach Network for Detecting Face Forgery Under Compression

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IEEE TRANSACTIONS ON MULTIMEDIA 2024年 26卷 2585-2597页

作者： Li, Congrui Zheng, Ziqiang Bin, Yi Wang, Guoqing Yang, Yang Li, Xuesheng Shen, Heng Tao Univ Elect Sci & Technol China Ctr Future Multimedia Chengdu 610054 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 610054 Peoples R China Univ Elect Sci & Technol China Sch Aeronaut & Astronaut Chengdu 610054 Peoples R China Peng Cheng Lab Shenzhen 518066 Peoples R China

The existing face forgery algorithms have achieved remarkable progress in how to generate reasonable facial images and can even successfully deceive human beings. Considering public security, face forgery detection is of vital importance, making it essential to design face forgery detection algorithms to detect forgery images over the Internet. Despite the great success achieved by the existing Deepfake detection algorithms, they usually failed to achieve satisfactory Deepfake detection performance when deployed to handle the forgery videos in practice. One significant reason is compression. The videos over the Internet are inevitably compressed considering the transmission efficiency. The video compression results in significant Deepfake detection performance degradation for the existing Deepfake detection algorithms. To address this issue, in this article, we propose a generic, simple yet effective "bleaching" pre-processing module based on the generative model and the high-level feature representations to produce a bleached image, which shares a similar appearance with the compressed images. The bleached images with recovered information can be identified accurately by the optimized Deepfake detection models without retraining. The proposed method has utilized a redesigned feature representation, which serves as a navigator to effectively and sufficiently alter the feature distribution in the high-dimensional space to remedy the difference between real facial images and forgery counterparts. Thus, the proposed method can successfully avoid misclassification. Comprehensive and extensive experiments are carried out on four low-quality Faceforensics++ datasets, demonstrating the effectiveness of our method in recovering the information loss caused by the compression artifacts across various backbones and compression.

关键词： Deepfakes Image coding Forgery Faces detection algorithms Feature extraction Generators Deepfake detection robust deepfake detection under compression adversarial learning

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Adaptive Attention-Based Graph Representation Learning to Detect Phishing Accounts on the Ethereum Blockchain

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第3期11卷 2963-2975页

作者： Sun, Haojie Liu, Zhaowei Wang, Shenqiang Wang, Haiyang Yantai Univ Sch Comp & Control Engn Yantai 264005 Peoples R China ICT Yantai Inst Network Technol Yantai 264003 Peoples R China

With Ethereum blockchain advancement, the Ethereum platform gathers numerous users. In this context, traditional phishing appears new fraud methods, resulting in significant losses. Currently, network embedding methods are considered effective solutions in the field of phishing detection. However, investigating existing Ethereum phishing node detection algorithms finds they are not optimal and still face two issues. Firstly, the Ethereum network's topology is unsatisfactory, with nodes exhibiting a long-tail distribution in their degree. Current technologies typically allow high-degree nodes to acquire high-quality embeddings, while low-degree nodes, constrained by limited structure, obtain embeddings of lower quality, significantly impacting the detection accuracy of downstream tasks. Secondly, different features of nodes will suffer losses during the fusion process, resulting in the final learned feature embedding being suboptimal. This paper presents an attention-based graphical representation learning approach (ABGRL) to address these problems. ABGRL extracts different feature information by means of multiple channels, and fuses the different feature information using adaptive attention convolution to select the feature information that has the greatest impact on the downstream task. Then the tail node feature information is enhanced by a self-supervised regression model with robust tail node embedding. Finally, the effectiveness of the proposed model was validated through extensive experiments.

关键词： Feature extraction Phishing Task analysis Ethernet detection algorithms Representation learning Tail Blockchain Ethereum graph neural networks network embedding phishing fraud detection

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Secure Control Design for Cooperative Adaptive Cruise Control Under False Data Injection Attack

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第8期25卷 9723-9732页

作者： Ansari-Bonab, Parisa Holland, James C. Cunningham-Rush, Jonas Noei, Shirin Sargolzaei, Arman Univ S Florida Dept Mech Engn Tampa FL 33620 USA Tennessee Technol Univ Dept Mech Engn Cookeville TN 38505 USA Univ Florida Dept Civil & Coastal Engn Gainesville FL 32611 USA

Cooperative adaptive cruise control (CACC) is one of the many advanced driver assistance systems (ADAS) that leverage communication between nearby vehicles to maintain speed while ensuring safe following distances. The current CACC algorithms are designed with the assumption that the communication channel is secure. However, the use of communication channels makes CACC susceptible to attacks, such as False data injection (FDI). This paper develops a novel secure nonlinear controller and a nonlinear observer which can estimate FDI attacks in real-time. Furthermore, this paper shows that the developed controller and FDI attack estimation techniques ensure semi-globally uniformly bounded tracking under FDI attacks, noise, and disturbances. The efficaciousness of the proposed CACC algorithm was demonstrated in simulation and through experimental implementation. During testing using both methodologies, the controller was able to maintain a safe following distance and estimate FDI attacks and noise in real-time.

关键词： Noise Noise measurement Real-time systems Observers Mathematical models detection algorithms Stability analysis Secure control design nonlinear observer attack estimation false data injection attack Lyapunov stability cooperative adaptive cruise control

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Hybrid Undersampling and Oversampling for Handling Imbalanced Credit Card Data

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IEEE ACCESS 2024年 12卷 14050-14060页

作者： Alamri, Maram Ykhlef, Mourad King Saud Univ Coll Comp & Informat Sci Informat Syst Dept Riyadh 11543 Saudi Arabia

Recent developments in the use of credit cards for a range of daily life activities have increased credit card fraud and caused huge financial losses for individuals and financial institutions. Most credit card frauds are conducted online through illegal payment authorizations by data breaches, phishing, or scams. Many solutions have been suggested for this issue, but they all face the major challenge of building an effective detection model using highly imbalanced class data. Most sampling techniques used for class imbalance have limitations, such as overlapping and overfitting, which cause inaccurate learning and are slowed down by noisy features. Herein, a hybrid Tomek links BIRCH Clustering Borderline SMOTE (BCBSMOTE) sampling method is proposed to balance a highly skewed credit card transaction dataset. First, Tomek links were used to undersample majority instances and remove noise, and then BIRCH clustering was applied to cluster the data and oversample minority instances using B-SMOTE. The credit card fraud-detection model was run using a random forest (RF) classifier. The proposed method achieved a higher F1-score (85.20%) than the baseline sampling techniques tested for comparison. Because of the enormous number of credit card transactions, there was still a small false-positive rate. The proposed method improves the detection performance owing to the well-organized balancing of the dataset.

关键词： Credit cards Fraud Data models Classification algorithms Synthetic data Support vector machines Null value detection algorithms Sampling methods Borderline SMOTE class imbalance credit card fraud detection sampling techniques Tomek links

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Quickest detection of False Data Injection Attacks in Smart Grid with Dynamic Models

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IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS 2022年第1期10卷 1292-1302页

作者： Nath, Samrat Akingeneye, Israel Wu, Jingxian Han, Zhu Univ Arkansas Dept Elect Engn Fayetteville AR 72701 USA Intel Corp San Diego CA 92131 USA Univ Houston Dept Elect & Comp Engn Houston TX 77204 USA Kyung Hee Univ Dept Comp Sci & Engn Seoul 446701 South Korea

A quickest intrusion detection algorithm is proposed to detect false data injection attacks (FDIAs) in smart grids with time-varying dynamic models. The quickest detection algorithm aims at minimizing the worst case detection delays (WDDs) of cyberattacks, subject to an upper bound of the false alarm rate. Since power-grid state transitions could be caused by either cyberattacks or sudden change in loads or grid configurations, we propose to distinguish between an FDIA and a sudden system change by using a time-varying dynamic model, which can accurately capture the dynamic state transitions due to changes in system configurations. A dynamic state estimation algorithm is developed to estimate and track the time-varying and nonstationary power-grid states. The quickest detection algorithm is developed by analyzing the statistical properties of dynamic state estimations, such that the algorithm minimizes the WDD while accurately distinguishing FDIA from sudden system changes. A Markov-chain-based analytical model is used to identify the detector's parameter and quantify its performance. Simulation results demonstrate that the proposed algorithm can accurately detect and remove false data injections or system faults with minimum delays. The proposed algorithm can be implemented to harden intelligent electronic devices (IEDs) or supervisory control and data acquisition (SCADA) systems to improve their resilience to cyberattacks or system faults, thus improving the cybersecurity of smart grids.

关键词： Power system dynamics Heuristic algorithms Delays State estimation Smart grids detection algorithms Cyberattack dynamic load change dynamic state estimation false data injection power system

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EPMDA-FED: Efficient and Privacy-Preserving Multidimensional Data Aggregation Scheme With Fast Error detection in Smart Grid

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IEEE INTERNET OF THINGS JOURNAL 2022年第9期9卷 6922-6933页

作者： Liu, Zhusen Cao, Zhenfu Dong, Xiaolei Zhao, Xiaopeng Liu, Tian Bao, Haiyong Shen, Jiachen East China Normal Univ Shanghai Key Lab Trustworthy Comp Shanghai 200062 Peoples R China Donghua Univ Sch Comp Sci & Technol Shanghai Peoples R China East China Normal Univ MoE Engn Res Ctr Software Hardware Codesign Techn Shanghai 200062 Peoples R China East China Normal Univ Software Engn Inst Shanghai 200062 Peoples R China Zhejiang Gongshang Univ Sch Comp Sci & Informat Engn Hangzhou 310018 Peoples R China

Smart grids bring advantages of reliability and high efficiency by real-time communication technologies in contrast to the traditional grids. However, smart grids also raise concerns about privacy and security for the individual fine-grained information collection. In order to guarantee privacy and security in the grids, we propose an efficient and privacy-preserving multidimensional data aggregation scheme without a third trusted party and supporting fast error detection, named EPMDA-FED, in the article. First, we adopt a Chinese Remainder Theorem (CRT) to pack multidimensional data and encrypt the processed data using the keys generated by the negotiation among users and the control center (CC). Second, our scheme is efficient for encryption without high-cost additive homomorphic public-key encryption (PKE) scheme, such as the Paillier cryptosystem and supporting batch verification with fast error detection. Our proposed error detection algorithm is quite efficient with sublinear computational complexity. Besides, through security analysis, EPMDA-FED is semantically secure against collusion attack and the consistency of negotiated key, authenticity, and data integrity of the users' reports are guaranteed. Finally, performance evaluation shows EPMDA-FED is more efficient than the existing competing approaches in terms of computational and communication overheads.

关键词： Data aggregation Smart grids Security Smart meters Costs Real-time systems detection algorithms Batch verification error detection multidimensional data aggregation privacy preserving smart grid

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