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Sparse adversarial learning for FDIA Attack Sample Generation in Distributed Smart

Computer Modeling in Engineering & Sciences 2024年第5期139卷 2095-2115页

作者： Fengyong Li Weicheng Shen Zhongqin Bi Xiangjing Su College of Computer Science and Technology Shanghai University of Electric PowerShanghai201306China College of Electrical Engineering Shanghai University of Electric PowerShanghai201306China

False data injection attack(FDIA)is an attack that affects the stability of grid cyber-physical system(GCPS)by evading the detecting mechanism of bad *** FDIA detection methods usually employ complex neural networkmodels to detect FDIA ***,they overlook the fact that FDIA attack samples at public-private network edges are extremely sparse,making it difficult for neural network models to obtain sufficient samples to construct a robust detection *** address this problem,this paper designs an efficient sample generative adversarial model of FDIA attack in public-private network edge,which can effectively bypass the detectionmodel to threaten the power grid system.A generative adversarial network(GAN)framework is first constructed by combining residual networks(ResNet)with fully connected networks(FCN).Then,a sparse adversarial learning model is built by integrating the time-aligned data and normal data,which is used to learn the distribution characteristics between normal data and attack data through iterative ***,we introduce a Gaussian hybrid distributionmatrix by aggregating the network structure of attack data characteristics and normal data characteristics,which can connect and calculate FDIA data with normal ***,efficient FDIA attack samples can be sequentially generated through interactive adversarial *** simulation experiments are conducted with IEEE 14-bus and IEEE 118-bus system data,and the results demonstrate that the generated attack samples of the proposed model can present superior performance compared to state-of-the-art models in terms of attack strength,robustness,and covert capability.

关键词： Distributed smart grid FDIA adversarial learning power public-private network edge

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Retinal Vessel Segmentation via adversarial learning and Iterative Refinement

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Journal of Shanghai Jiaotong university(Science) 2024年第1期29卷 73-80页

作者：顾闻徐奕 School of Electronic Information and Electrical Engineering Shanghai Jiao Tong UniversityShanghai 200240China

Retinal vessel segmentation is a challenging medical task owing to small size of dataset,micro blood vessels and low image *** address these issues,we introduce a novel convolutional neural network in this paper,which takes the advantage of both adversarial learning and recurrent neural *** iterative design of network with recurrent unit is performed to refine the segmentation results from input retinal image *** unit preserves high-level semantic information for feature reuse,so as to output a sufficiently refined segmentation map instead of a coarse ***,an adversarial loss is imposing the integrity and connectivity constraints on the segmented vessel regions,thus greatly reducing topology errors of *** experimental results on the DRIVE dataset show that our method achieves area under curve and sensitivity of 98.17%and 80.64%,*** method achieves superior performance in retinal vessel segmentation compared with other existing state-of-the-art methods.

关键词： medical image processing retinal image segmentation adversarial learning iterative refinement

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Link-Privacy Preserving Graph Embedding Data Publication with adversarial learning

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Tsinghua Science and Technology 2022年第2期27卷 244-256页

作者： Kainan Zhang Zhi Tian Zhipeng Cai Daehee Seo the Department of Computer Science Georgia State UniversityAtlantaGA 30303USA the Department of Electrical and Computer Engineering George Mason UniversityFairfaxVA 22030USA the National Center of Excellence in Software Sangmyung UniversitySeoul 03016Republic of Korea

The inefficient utilization of ubiquitous graph data with combinatorial structures necessitates graph embedding methods,aiming at learning a continuous vector space for the graph,which is amenable to be adopted in traditional machine learning algorithms in favor of vector *** embedding methods build an important bridge between social network analysis and data analytics,as social networks naturally generate an unprecedented volume of graph data *** social network data not only brings benefit for public health,disaster response,commercial promotion,and many other applications,but also gives birth to threats that jeopardize each individual’s privacy and ***,most existing works in publishing social graph embedding data only focus on preserving social graph structure with less attention paid to the privacy issues inherited from social *** be specific,attackers can infer the presence of a sensitive relationship between two individuals by training a predictive model with the exposed social network *** this paper,we propose a novel link-privacy preserved graph embedding framework using adversarial learning,which can reduce adversary’s prediction accuracy on sensitive links,while persevering sufficient non-sensitive information,such as graph topology and node attributes in graph *** experiments are conducted to evaluate the proposed framework using ground truth social network datasets.

关键词： graph embedding privacy preservation adversarial learning

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Improving Open Set Domain Adaptation Using Image-to-Image Translation and Instance-Weighted adversarial learning

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Journal of Computer Science & Technology 2023年第3期38卷 644-658页

作者：张鸿杰李昂过洁郭延文 State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China Google DeepMind Mountain ViewCA 94043U.S.A.

We propose to address the open set domain adaptation problem by aligning images at both the pixel space and the feature *** approach,called Open Set Translation and Adaptation Network(OSTAN),consists of two main components:translation and *** translation is a cycle-consistent generative adversarial network,which translates any source image to the“style”of a target domain to eliminate domain discrepancy in the pixel *** adaptation is an instance-weighted adversarial network,which projects both(labeled)translated source images and(unlabeled)target images into a domain-invariant feature space to learn a prior probability for each target *** learned probability is applied as a weight to the unknown classifier to facilitate the identification of the unknown *** proposed OSTAN model significantly outperforms the state-of-the-art open set domain adaptation methods on multiple public *** experiments also demonstrate that both the image-to-image translation and the instance-weighting framework can further improve the decision boundaries for both known and unknown classes.

关键词： adversarial learning domain adaptation open set translation

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Improving Knowledge Graph Completion Using Soft Rules and adversarial learning

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Chinese Journal of Electronics 2021年第4期30卷 623-633页

作者： TANG Caifang RAO Yuan YU Hualei SUN Ling CHENG Jiamin WANG Yutian Xi'an Key Laboratory of Social Intelligence and Complexity Data Processing School of Software EngineeringXi'an Jiaotong University Shaanxi Joint Key Laboratory for Artifact Intelligence School of Software EngineeringXi'an Jiaotong University School of Software Engineering Xi'an Jiaotong University

Knowledge graph is a useful resources and tools for describing entities and relationships in natural language processing tasks. However, the existing knowledge graph are incomplete. Therefore, knowledge graph completion technology has become a research hotspot in the field of artificial intelligence, but the traditional knowledge graph embedding method does not fully take into account the role of logic rules and the effect of false negative samples on knowledge *** on the logic rules of knowledge and the role of adversarial learning in knowledge embedding, we proposes a model to improve the completion of knowledge graph:soft Rules and graph adversarial learning(RUGA). Firstly,the traditional knowledge graph embedding model is trained as generator and discriminator by using adversarial learning method, and high-quality negative samples are obtained. Then these negative samples and the existing positive samples together constitute the label triple in the injection rule model. The whole model will benefit from both high-quality samples and logical rules. In addition,we evaluated the performance of link prediction task and triple classification task on Freebase and Yago datasets respectively. Finally, the experimental results show that the model can effectively improve the effect of knowledge graph completion.

关键词： Knowledge graph Knowledge embedding Knowledge graph completion adversarial learning

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adversarial Active learning for Named Entity Recognition in Cybersecurity

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Computers, Materials & Continua 2021年第1期66卷 407-420页

作者： Tao Li Yongjin Hu Ankang Ju Zhuoran Hu Zhengzhou Institute of Information Science and Technology Zhengzhou450001China College of Letters and Science University of Wisconsin-MadisonMadison53706USA

Owing to the continuous barrage of cyber threats,there is a massive amount of cyber threat ***,a great deal of cyber threat intelligence come from textual *** analysis of cyber threat intelligence,many security analysts rely on cumbersome and time-consuming manual *** knowledge graph plays a significant role in automatics analysis of cyber threat *** the foundation for constructing cybersecurity knowledge graph,named entity recognition(NER)is required for identifying critical threat-related elements from textual cyber threat ***,deep neural network-based models have attained very good results in ***,the performance of these models relies heavily on the amount of labeled *** labeled data in cybersecurity is scarce,in this paper,we propose an adversarial active learning framework to effectively select the informative samples for further *** addition,leveraging the long short-term memory(LSTM)network and the bidirectional LSTM(BiLSTM)network,we propose a novel NER model by introducing a dynamic attention mechanism into the BiLSTM-LSTM *** the selected informative samples annotated,the proposed NER model is *** a result,the performance of the NER model is incrementally enhanced with low labeling *** results show the effectiveness of the proposed method.

关键词： adversarial learning active learning named entity recognition dynamic attention mechanism

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Multi-view Feature learning for the Over-penalty in adversarial Domain Adaptation

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Data Intelligence 2024年第1期6卷 183-200页

作者： Yuhong Zhang Jianqing Wu Qi Zhang Xuegang Hu School of Computer and Information Engineering Hefei University of TechnologyHefei 230601China Key Laboratory of Knowledge Engineering with Big Data(Hefei University of Technology) The Ministry of Education of ChinaHefei 230009China

Domain adaptation aims to transfer knowledge from the labeled source domain to an unlabeled target domain that follows a similar but different ***,adversarial-based methods have achieved remarkable success due to the excellent performance of domain-invariant feature presentation ***,the adversarial methods learn the transferability at the expense of the discriminability in feature representation,leading to low generalization to the target *** this end,we propose a Multi-view Feature learning method for the Over-penalty in adversarial Domain ***,multi-view representation learning is proposed to enrich the discriminative information contained in domain-invariant feature representation,which will counter the over-penalty for discriminability in adversarial ***,the class distribution in the intra-domain is proposed to replace that in the inter-domain to capture more discriminative information in the learning of transferrable *** experiments show that our method can improve the discriminability while maintaining transferability and exceeds the most advanced methods in the domain adaptation benchmark datasets.

关键词： domain adaptation adversarial learning multi-view learning

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Lightweight Super-Resolution Model for Complete Model Copyright Protection

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Tsinghua Science and Technology 2024年第4期29卷 1194-1205页

作者： Bingyi Xie Honghui Xu YongJoon Joe Daehee Seo Zhipeng Cai Department of Computer Science Georgia State UniversityAtlantaGA 30303USA LSWare Inc. Seoul 08504Republic of Korea College of Intelligence Information Engineering Sangmyung UniversityRepublic of KoreaSeoul 03016

Deep learning based techniques are broadly used in various applications, which exhibit superior performance compared to traditional methods. One of the mainstream topics in computer vision is the image super-resolution task. In recent deep learning neural networks, the number of parameters in each convolution layer has increased along with more layers and feature maps, resulting in better image super-resolution performance. In today’s era, numerous service providers offer super-resolution services to users, providing them with remarkable convenience. However, the availability of open-source super-resolution services exposes service providers to the risk of copyright infringement, as the complete model could be vulnerable to leakage. Therefore, safeguarding the copyright of the complete model is a non-trivial concern. To tackle this issue, this paper presents a lightweight model as a substitute for the original complete model in image super-resolution. This research has identified smaller networks that can deliver impressive performance, while protecting the original model’s copyright. Finally, comprehensive experiments are conducted on multiple datasets to demonstrate the superiority of the proposed approach in generating super-resolution images even using lightweight neural networks.

关键词： lightweight adversarial learning image super-resolution copyright protection

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Corporate Credit Ratings Based on Hierarchical Heterogeneous Graph Neural Networks

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Machine Intelligence Research 2024年第2期21卷 257-271页

作者： Bo-Jing Feng Xi Cheng Hao-Nan Xu Wen-Fang Xue Center for Research on Intelligent Perception and Computing National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing 100190China

order to help investors understand the credit status of target corporations and reduce investment risks,the corporate credit rating model has become an important evaluation tool in the financial *** models are based on statistical learning,machine learning and deep learning especially graph neural networks(GNNs).However,we found that only few models take the hierarchy,heterogeneity or unlabeled data into account in the actual corporate credit rating ***,we propose a novel framework named hierarchical heterogeneous graph neural networks(HHGNN),which can fully model the hierarchy of corporate features and the heterogeneity of relationships between *** addition,we design an adversarial learning block to make full use of the rich unlabeled samples in the financial *** experiments conducted on the public-listed corporate rating dataset prove that HHGNN achieves SOTA compared to the baseline methods.

关键词： Corporate credit rating hierarchical relation heterogeneous graph neural networks adversarial learning

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adversarial data splitting for domain generalization

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Science China(Information Sciences) 2024年第5期67卷 28-42页

作者： Xiang GU Jian SUN Zongben XU School of Mathematics and Statistics Xi'an Jiaotong University

Domain generalization aims to learn a model that is generalizable to an unseen target domain,which is a fundamental and challenging task in machine learning for out-of-distribution generalization. This paper proposes a novel domain generalization approach that enforces the learned model to be able to generalize well over the train/val subset splitting of the training dataset. This idea is modeled herein as an adversarial data splitting framework, formulated as a min-max optimization problem inspired by the metalearning approach. The min-max optimization problem is solved by iteratively splitting the training dataset into the training and val subsets to maximize the domain shift measured by the objective function and updating the model parameters to enable the model to generalize well from the training subset to the val subset by minimizing the objective function. This adversarial training approach does not assume the known domain labels of the training data; instead, it automatically investigates the “hard” splitting of the train/val subsets to learn the generalizable model. Extensive experimental results using three benchmark datasets demonstrate the superiority of this approach. In addition, we derive a generalization error bound for the theoretical understanding of our proposed approach.

关键词： domain generalization adversarial learning data splitting meta-learning out of distribution

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