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AIGCN: Attack Intention Detection for Power System Using graph convolutional networks

JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY 2022年第11期94卷 1119-1127页

作者： Tang, Qiuhang Chen, Huadong Ge, Binbin Wang, Haoyu China Energy Engn Grp ZHEJIANG Elect Power Design Hangzhou Zhejiang Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data & Brain Comp Beijing Peoples R China Univ Delaware Sch Comp Sci & Engn Delaware OH USA

Power systems have been attracting the attention of attackers because of its great value. Identifying attack intentions is essential for proactively blocking the intrusion into power information systems. In this paper, we propose AIGCN, a novel attack intention detection model based on graph convolutional networks. Particularly, AIGCN first presents an abnormal IP detection method based on log behavior analysis to filter suspicious malicious IPs. And then AIGCN models the interactive relationships between suspicious IPs as a graph and performs graph convolution operation on the graph to effectively detect the attack intentions and learn the attack patterns with different intentions. Experimental results on real-world datasets verify that AIGCN outperforms baseline methods in detecting attack intentions and demystifying corresponding attack patterns.

关键词： Power system Attack intention detection Intrusion detection graph convolutional networks

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MAGE: Automatic diagnosis of autism spectrum disorders using multi-atlas graph convolutional networks and ensemble learning

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NEUROCOMPUTING 2022年 469卷 346-353页

作者： Wang, Yufei Liu, Jin Xiang, Yizhen Wang, Jianxin Chen, Qingyong Chong, Jing Cent South Univ Sch Comp Sci & Engn Hunan Prov Key Lab Bioinformat Changsha 410083 Peoples R China China Mobile Chengdu Ind Res Inst Chengdu 610041 Peoples R China Mobile Hlth Minist Educ China Mobile Joint Lab Changsha 410008 Peoples R China

Currently, it is still a great challenge in clinical practice to accurately diagnose autism spectrum disorder (ASD). To address this challenge, in this study we propose a method for automatic diagnosis of ASD based on multi-atlas graph convolutional networks and ensemble learning. Firstly, we extract multiple feature representations based on functional connectivity (FC) of different brain atlases from fMRI data of each subject. Then, to obtain the features that are more helpful for ASD automatic diagnosis, we propose a multi-atlas graph convolutional network method (MAGCN). Finally, to combine different feature representations, we propose a stacking ensemble learning method to perform the final ASD automatic diagnostic task. Our proposed method is evaluated on 949 subjects (including 419 subjects with ASD and 530 subjects with typical control (TC)) from the Autism Brain Imaging Data Exchange (ABIDE). Experimental results show that our proposed method achieves an accuracy of 75.86% and an area under the receiver operating characteristic curve (AUC) of 0.8314 for automatic diagnosis of ASD. In addition, compared with some methods published in recent years, our proposed method obtains the best performance of ASD diagnosis. Overall, our proposed method is effective and promising for automatic diagnosis of ASD in clinical practice. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Autism spectrum disorders Automatic diagnosis Functional connectivity Multi-atlas graph convolutional networks Ensemble learning

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AMalNet: A deep learning framework based on graph convolutional networks for malware detection

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COMPUTERS & SECURITY 2020年 93卷

作者： Pei, Xinjun Yu, Long Tian, Shengwei Xinjiang Univ Sch Informat Sci & Engn Urumqi 830001 Xinjiang Peoples R China Xinjiang Univ Network Ctr Urumqi 830001 Xinjiang Peoples R China Xinjiang Univ Sch Software Urumqi 830001 Xinjiang Peoples R China

The increasing popularity of Android apps attracted widespread attention from malware authors. Traditional malware detection systems suffer from some shortcomings;computationally expensive, insufficient performance or not robust enough. To address this challenge, we (1) build a novel and highly reliable deep learning framework, named AMalNet, to learn multiple embedding representations for Android malware detection and family attribution, (2) introduce a version of graph convolutional networks (GCNs) for modeling high-level graphical semantics, which automatically identifies and learns the semantic and sequential patterns, (3) use an Independently Recurrent Neural Network (IndRNN) to decode the deep semantic information, making full use of remote dependent information between nodes to independently extract features. The experimental results on multiple benchmark datasets indicated that the AMalNet framework outperforms other state-of-the-art techniques significantly. (C) 2020 Published by Elsevier Ltd.

关键词： Word embedding graph convolutional networks Independently recurrent neural networks Android Malware detection Static analysis

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Copy-Move Forgery Detection Technique Using graph convolutional networks Feature Extraction

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IEEE ACCESS 2024年 12卷 121675-121687页

作者： Shinde, Varun Dhanawat, Vineet Almogren, Ahmad Biswas, Anjanava Bilal, Muhammad Naqvi, Rizwan Ali Ur Rehman, Ateeq Cloudera Inc Austin TX 78701 USA Meta Platforms Inc Menlo Pk CA 94025 USA King Saud Univ Coll Comp & Informat Sci Dept Comp Sci Riyadh 11633 Saudi Arabia AWS AI & Machine Learning San Diego CA 92129 USA HITEC Univ Dept Comp Engn Taxila 47040 Pakistan Sejong Univ Dept AI & Robot Seoul 05006 South Korea Gachon Univ Sch Comp Seongnam 13120 South Korea

In the development of image forensics, detection of Copy-Move Forgery (CMF) has become a major challenge due to the proliferation of image forgery techniques. The CMF is widely utilized to alter the content of the original image to spread false information or to use such forged digital images for illegal purposes e.g. false evidence in the court of law, or to blackmailing any individual. This paper presents a new method for CMF Detection (CMFD) that uses the power of graph Convolution networks (GCNs) and its multiple layers with ReLU activation, for CMFD and analysis. The aim to use GCN is due to its ability to improve the feature extraction process by utilizing the spatial and structural affiliation between elements in the digital images. Also, the GCN aims to store information about images and use it to graphically describe images with pixels or image areas as features, spatial and correlation relationships as edges. By pulling data from this image, GCN is able to obtain content rich features that are very powerful at detecting CMF regions. In proposed methodology, we utilized Support Vector machine (SVM) for classification and the binary cross-entropy loss, and the Adam optimizer for improving accuracy. Our scheme successfully achieves high accuracy and is effective in CMFD. We use the MICC F220, and CoMoFoD datasets to test the GCN in our proposed CMFD method. Through much testing and evaluation, we have found that GCN has the tremendous ability for CMFD in digital images in term of accuracy.

关键词： Feature extraction Digital images Accuracy Forgery Image forensics Image edge detection Transforms Forensics graph convolutional networks CMFD forensic science feature extraction GCN

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Spatial-temporal short-term load forecasting framework via K-shape time series clustering method and graph convolutional networks

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ENERGY REPORTS 2022年 8卷 8752-8766页

作者： Wu, Zeqing Mu, Yunfei Deng, Shuai Li, Yang Tianjin Univ Key Lab Efficient Utilizat Low & Medium Grade Ener MOE Tianjin 300350 Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

Load forecasting, especially short-term load forecasting, is of great significance to the safe operation of power grids and power system optimization. Historical data is commonly applied in the most of existing methods of load forecasting to predict the future data in one time step, which is considered to have a positive effect on the forecasting performance. However, a serious problem is ignored, which is the assumption that the interactions between end-users are often not considered. Such interaction could be considered as a paired non-Euclidean relationship, which could enhance the accuracy of forecasting effects. In this paper, we propose a graph convolutional network prediction framework based on K-shape time series clustering method for short-term load forecasting. User clusters with similar electricity consumption habits could be separated from a large number of users by the proposed framework. Simultaneously, the paired non-Euclidean relationship between users can also be captured by this framework. The proposed framework is evaluated by the dataset of business users in the southeastern United States. The results show that compared with baselines, the framework significantly improves the prediction accuracy in any scale of user groups, as well as future data in one time step and multi-time steps.(c) 2022 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Electric load forecasting Spatial-temporal Time series clustering graph convolutional networks

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Dual-domain graph convolutional networks for skeleton-based action recognition

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MACHINE LEARNING 2022年第7期111卷 2381-2406页

作者： Chen, Shuo Xu, Ke Mi, Zhongjie Jiang, Xinghao Sun, Tanfeng Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China

Skeleton-based action recognition is attracting more and more attention owing to the general representation ability of skeleton data. The graph convolutional networks (GCNs) methods extended from convolutional Neural networks (CNNs) are proposed to directly extract spatial-temporal information from the graphs. Previous GCNs usually aggregate the skeleton information locally in the vertex domain. However, the focus on the local information brought about the limited representation ability in some actions containing overall dynamics in both spatial and temporal, which pulled down the overall accuracy of the model. Therefore, this paper proposes a more comprehensive two-stream GCN architecture containing the vertex-domain graph convolution and the spectral graph convolution based on graph Fourier Transform (GFT). One stream utilizes an efficient vertex-domain graph convolution to obtain effective spatial-temporal information via graph Shift Blocks (GSB), while the other brings the global spectral information from our improved Residual Spectral Blocks (RSB). According to the analysis of the experimental results, the action misalignment for certain actions is reduced. Moreover, along with other GCN methods that only focus on spatial-temporal information, our RSB strategies help improve their performance. DD-GCN is evaluated on three large skeleton-based datasets, NTU-RGBD 60, NTU-RGBD 120, and Kinetics-Skeleton. The experiment results demonstrate a comparable ability to the state-of-the-art.

关键词： Action recognition Skeleton graph convolutional networks Dual-domain Spatial-temporal Spectral

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Exploiting node-feature bipartite graph in graph convolutional networks

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INFORMATION SCIENCES 2023年 628卷 409-423页

作者： Jiang, Yuli Lin, Huaijia Li, Ye Rong, Yu Cheng, Hong Huang, Xin Chinese Univ Hong Kong Hong Kong Peoples R China Hong Kong Baptist Univ Hong Kong Peoples R China Tencent AI Lab Shen Zhen Peoples R China

In recent years, graph convolutional networks (GCNs), which extend convolutional neural networks to graph structure, have achieved great success on many graph learning tasks by fusing structure and feature information, such as node classification. However, the graph structure is constructed from real-world data and usually contains noise or redundancy. In addition, this structural information is based on manually defined relations and is not potentially optimal for downstream tasks. In this paper, we utilize the knowledge from node features to enhance the expressive power of GCN models in a plug-and-play fashion. Specifically, we build a node-feature bipartite graph and exploit the bipartite graph convolutional network to model node-feature relations. By aligning results from the original graph structure and node-feature relations, we can make a more accurate prediction for each node in an end-to-end manner. Extensive experiments demonstrate that the proposed model can extract knowledge from two branches and improve the performance of various GCN models on typical graph data sets and 3D point cloud data.

关键词： graph convolutional networks Bipartite graph Bipartite graph convolutional networks Semi-supervised learning Node classification

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Landmark Localization for Cephalometric Analysis Using Multiscale Image Patch-Based graph convolutional networks

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2022年第7期26卷 3015-3024页

作者： Lu, Gang Zhang, Yuanxiu Kong, Youyong Zhang, Chen Coatrieux, Jean-Louis Shu, Huazhong Southeast Univ Lab Image Sci & Technol Nanjing 210096 Peoples R China Ctr Rech Informat Biomed Sino Francais F-35000 Rennes France Southeast Univ Jiangsu Prov Joint Int Res Lab Med Informat Proc Nanjing 210096 Peoples R China Nanjing Med Univ Jiangsu Prov Engn Res Ctr Stomatol Translat Med Affiliated Stomatol Hosp Nanjing 210029 Peoples R China Univ Rennes 1 Lab Traitement Signal & Image F-35000 Rennes France Natl Inst Hlth & Med Res F-35000 Rennes France

Accurate and robust cephalometric image analysis plays an essential role in orthodontic diagnosis, treatment assessment and surgical planning. This paper proposes a novel landmark localization method for cephalometric analysis using multiscale image patch-based graph convolutional networks. In detail, image patches with the same size are hierarchically sampled from the Gaussian pyramid to well preserve multiscale context information. We combine local appearance and shape information into spatialized features with an attention module to enrich node representations in graph. The spatial relationships of landmarks are built with the incorporation of three-layer graph convolutional networks, and multiple landmarks are simultaneously updated and moved toward the targets in a cascaded coarse-to-fine process. Quantitative results obtained on publicly available cephalometric X-ray images have exhibited superior performance compared with other state-of-the-art methods in terms of mean radial error and successful detection rate within various precision ranges. Our approach performs significantly better especially in the clinically accepted range of 2 mm and this makes it suitable in cephalometric analysis and orthognathic surgery.

关键词： Location awareness Biomedical imaging Heating systems Feature extraction Shape X-rays Image edge detection Cephalometric analysis graph convolutional networks landmark localization multiscale

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An Attention-Driven Multi-label Image Classification with Semantic Embedding and graph convolutional networks

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COGNITIVE COMPUTATION 2023年第4期15卷 1308-1319页

作者： Sun, Dengdi Ma, Leilei Ding, Zhuanlian Luo, Bin Anhui Univ Sch Artificial Intelligence Key Lab Intelligent Comp & Signal Proc ICSP Minist Educ Hefei 230601 Peoples R China Anhui Univ Sch Comp Sci & Technol Anhui Prov Key Lab Multimodal Cognit Comp Hefei 230601 Peoples R China Anhui Univ Sch Internet Hefei 230039 Peoples R China

Multi-label image classification is a fundamental and vital task in computer vision. The latest methods are mostly based on deep learning and exhibit excellent performance in understanding images. However, in previous studies, only capture the image content information has been captured using convolutional neural networks (CNNs), and the semantic structure information and implicit dependencies between labels and image regions have been ignored. Therefore, it is necessary to develop more effective methods for integrating semantic information and visual features in multi-label image classification. In this study, we propose a novel framework for multi-label image classification, named FLNet, which simultaneously takes advantage of the visual features and semantic structure. Specifically, to enhance the association between semantic annotations and image regions, we first integrate the attention mechanism with a CNN to focus on the target regions while ignoring other useless surrounding information and then employ graph convolutional network (GCN) to capture the structure information between multiple labels. Based on our architecture, we also introduce the lateral connections to repeatedly inject the label system into the CNN backbone during the GCN learning process to improve performance and, consequently, learn interdependent classifiers for each image label. We apply our method to multi-label image classification. The experiments on two public multi-label benchmark datasets, namely, MS-COCO and PASCAL visual object classes challenge (VOC 2007), demonstrate that our approach outperforms other existing state-of-the-art methods. Our method learns specific target regions and enhances the association between labels and image regions by using semantic information and attention mechanism. Thus, we combine the advantages of both visual and semantic information to further improve the image classification performance. Finally, the correctness and effectiveness of the proposed

关键词： graph convolutional networks Multi-label classification Semantic embedding Attention mechanism

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Simplified multilayer graph convolutional networks with dropout

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APPLIED INTELLIGENCE 2022年第5期52卷 4776-4791页

作者： Yang, Fei Zhang, Huyin Tao, Shiming Wuhan Univ Sch Comp Sci Wuhan Peoples R China Minist Nat Resources Key Lab Urban Land Resources Monitoring & Simulat Shenzhen Peoples R China

graph convolutional networks (GCNs) and their variants are excellent deep learning methods for graph-structured data. Moreover, multilayer GCNs can perform feature smoothing repeatedly, which creates considerable performance improvements. However, they may inherit unnecessary complexity and redundant computation;to make matters worse, they introduce overfitting as the number of layers increases. In this paper, we present simplified multilayer graph convolutional networks with dropout (DGCs), novel neural network architectures that successively perform nonlinearity removal and weight matrix merging between graph conventional layers, leveraging a dropout layer to achieve feature augmentation and effectively reduce overfitting. Under such circumstances, first, we extend a shallow GCN to a multilayer GCN. Then, we reduce the complexity and redundant calculations of the multilayer GCN, while improving its classification performance. Finally, we make DGCs readily applicable to inductive and transductive tasks. Extensive experiments on citation networks and social networks offer evidence that the proposed model matches or outperforms state-of-the-art methods.

关键词： graph convolutional networks Multilayer Dropout Feature augmentation

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