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Insider threat detection approach for tobacco industry based on heterogeneous graph embedding

High Technology Letters 2024年第2期30卷 199-210页

作者：季琦 LI Wei PAN Bailin XUE Hongkai QIU Xiang Hangzhou Cigarette Factory China Tobacco Zhejiang Industrial Co.Ltd.Hangzhou 310024P.R.China Information Center China Tobacco Zhejiang Industrial Co.Ltd.Hangzhou 310024P.R.China College of Information Engineering Zhejiang University of TechnologyHangzhou 310014P.R.China

In the tobacco industry,insider employee attack is a thorny problem that is difficult to detect.To solve this issue,this paper proposes an insider threat detection method based on heterogeneous graph embedding.First,the interrelationships between logs are fully considered,and log entries are converted into heterogeneous graphs based on these relationships.Second,the heterogeneous graph embedding is adopted and each log entry is represented as a low-dimensional feature vector.Then,normal logs and malicious logs are classified into different clusters by clustering algorithm to identify malicious logs.Finally,the effectiveness and superiority of the method is verified through experiments on the CERT dataset.The experimental results show that this method has better performance compared to some baseline methods.

关键词： insider threat detection advanced persistent threats graph construction heterogeneous graph embedding

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Relational graph location network for multi-view image localization

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系统工程与电子技术：英文版 2023年第2期34卷 460-468页

作者： YANG Yukun LIU Xiangdong School of Automation Beijing Institute of TechnologyBeijing 100081China

In multi-view image localization task,the features of the images captured from different views should be fused properly.This paper considers the classification-based image localization problem.We propose the relational graph location network(RGLN)to perform this task.In this network,we propose a heterogeneous graph construction approach for graph classification tasks,which aims to describe the location in a more appropriate way,thereby improving the expression ability of the location representation module.Experiments show that the expression ability of the proposed graph construction approach outperforms the compared methods by a large margin.In addition,the proposed localization method outperforms the compared localization methods by around 1.7%in terms of meter-level accuracy.

关键词： multi-view image localization graph construction heterogeneous graph graph neural network

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Dimensionality reduction with adaptive graph

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Frontiers of Computer Science 2013年第5期7卷 745-753页

作者： Lishan QIAO Limei ZHANG Songcan CHEN Department of Mathematics Science Liaocheng University Liaocheng 252000 China Department of Computer Science and Engineering Nanjing University of Aeronautics & Astronautics Nanjing 210016 China

graph-based dimensionality reduction （DR） methods have been applied successfully in many practical problems, such as face recognition, where graphs play a crucial role in modeling the data distribution or structure. However, the ideal graph is, in practice, difficult to discover. Usually, one needs to construct graph empirically according to various motivations, priors, or assumptions; this is inde- pendent of the subsequent DR mapping calculation. Different from the previous works, in this paper, we attempt to learn a graph closely linked with the DR process, and propose an al- gorithm called dimensionality reduction with adaptive graph （DRAG）, whose idea is to, during seeking projection matrix, simultaneously learn a graph in the neighborhood of a pre- specified one. Moreover, the pre-specified graph is treated as a noisy observation of the ideal one, and the square Frobenius divergence is used to measure their difference in the objective function. As a result, we achieve an elegant graph update for- mula which naturally fuses the original and transformed data information. In particular, the optimal graph is shown to be a weighted sum of the pre-defined graph in the original space and a new graph depending on transformed space. Empirical results on several face datasets demonstrate the effectiveness of the proposed algorithm.

关键词： Dimensionality reduction graph construction face recognition

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Nearest-neighbor classifier motivated marginal discriminant projections for face recognition

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Frontiers of Computer Science 2011年第4期5卷 419-428页

作者： Pu HUANG Zhenmin TANG Caikou CHEN Xintian CHENG School of Computer Science and Technology Nanjing University of Science and Technology Nanjing 210094 China College of Information Engineering Yangzhou University Yangzhou 225009 China

Marginal Fisher analysis （MFA） is a repre- sentative margin-based learning algorithm for face recognition. A major problem in MFA is how to select appropriate parameters, k1 and k2, to construct the respective intrinsic and penalty graphs. In this paper, we propose a novel method called nearest-neighbor （NN） classifier motivated marginal discriminant projections （NN-MDP）. Motivated by the NN classifier, NN-MDP seeks a few projection vectors to prevent data samples from being wrongly categorized. Like MFA, NN-MDP can characterize the compactness and separability of samples simultaneously. Moreover, in contrast to MFA, NN-MDP can actively construct the intrinsic graph and penalty graph without unknown parameters. Experimental results on the 0RL, Yale, and FERET face databases show that NN-MDP not only avoids the intractability, and high expense of neighborhood parameter selection, but is also more applicable to face recognition with NN classifier than other methods.

关键词： dimensionality reduction （DR） face recogni-tion marginal Fisher analysis （MFA） locality preservingprojections （LPP） graph construction margin-based,nearest-neighbor （NN） classifier

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Local Preserving graphs Using Intra-Class Competitive Representation for Dimensionality Reduction of Hyperspectral Image

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Journal of Beijing Institute of Technology 2021年第2期30卷 139-158页

作者： Zhen Ye Shihao Shi Tao Sun Lin Bai School of Electronics and Control Engineering Chang’an UniversityXi’an 710064China

As a key technique in hyperspectral image pre-processing,dimensionality reduction has received a lot of attention.However,most of the graph-based dimensionality reduction methods only consider a single structure in the data and ignore the interfusion of multiple structures.In this paper,we propose two methods for combining intra-class competition for locally preserved graphs by constructing a new dictionary containing neighbourhood information.These two methods explore local information into the collaborative graph through competing constraints,thus effectively improving the overcrowded distribution of intra-class coefficients in the collaborative graph and enhancing the discriminative power of the algorithm.By classifying four benchmark hyperspectral data,the proposed methods are proved to be superior to several advanced algorithms,even under small-sample-size conditions.

关键词： intra-class competition graph construction hyperspectral image dimensionality reduction

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