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graph learning on Instruction Stream-Augmented CFG for Malware Variant Detection

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2025年 20卷 3015-3030页

作者： Mi, Jiaxin Li, Qi Han, Zewei Liao, Weilue Fu, Junsong Beijing Univ Posts & Telecommun Sch Cyberspace Secur Beijing 100876 Peoples R China China Informat Technol Secur Evaluat Ctr Beijing 100085 Peoples R China

As malware as a service (MaaS) and organized attacks develop and drive a shift in malware variant generation mechanism, current variant detection, designed to counter conventional obfuscation and anti-detection strategies, falls short in facing new challenges, particularly in identifying variants that maintain core functionalities while altering local behaviors, or those sharing similar code logic but diverge in actual functionalities. To tackle the problems, we present ISCMVD, an Instruction Stream-augmented CFG-based Malware Variant Detection scheme, melding control flow structures with machine semantic information from instruction streams within blocks to build a comprehensive functional representation for variants' basic and detailed behaviors. Leveraging a global-enhanced attentive graph neural network to integrate local and global functional features, we significantly boost the capture of representative stable primary behaviors' similarity from variants within the same family identifying variants generated under attackers' code rewriting, module modification, and other transformation means. Additionally, through cross-family associative analysis, we eliminate classification interference of variants' logic similarities stemming from the same organization generating. Evaluation results on public and real-world datasets demonstrate the superiority and robustness of ISCMVD with an average of 99.29% in AC and 99.25% in F1 and perform well even in few-shot cases. What's more important, we achieve a breakthrough in two special sample sets including variants related to MaaS and APT group, and outperform state-of-the-art methods under the current variant generation mechanism, proving its suitability for future trends.

关键词： Feature extraction Malware Encoding Codes Logic Market research Streams Semantics Interference Data mining Variant generation mechanism malware variant detection variant functionality control flow graph graph learning

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graph learning With Riemannian Optimization for Multi-View Integrative Clustering

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年第1期9卷 381-393页

作者： Khan, Aparajita Maji, Pradipta Indian Stat Inst Machine Intelligence Unit Kolkata 700108 India Indian Inst Technol Roorkee Dept Comp Sci & Engn Roorkee 247667 India Indian Stat Inst Machine Intelligence Unit Biomed Imaging & Bioinformat Lab Kolkata 700108 India

Real-world multi-view data may manifest as point-clouds, but their meaningful structure often resides on a lower dimensional manifold embedded in the higher dimensional space. Consequently, existing graph based multi-view algorithms focus primarily on extraction of the low-rank subspaces for clustering. However, simultaneous optimization of the individual graph structures, their contributions/weights along with the clustering subspaces is likely to give a more comprehensive idea of the clusters present in the data set. In this regard, the paper proposes a Riemannian manifold optimization algorithm that harnesses the geometry and structure preserving properties of symmetric positive definite (SPD) manifold and Grassmann manifold for efficient multi-view clustering. The SPD manifold is used to optimize the graph Laplacians corresponding to the individual views, while preserving their symmetricity, positive definiteness, and related properties. The Grassmann manifold, on the other hand, is used to minimize the disagreement between the joint and individual clustering subspaces. Optimization over the Grassmann manifold additionally enforces the clustering solutions to be basis invariant such that all cluster indicator matrices whose columns span the same subspace map to the same clustering solution. A gradient based line-search algorithm, which alternates between two different manifolds, is proposed to optimize the multi-view graph Laplacians and its associated subspaces. The matrix perturbation theory is used to theoretically bound the disagreement between the clustering subspaces. Extensive experiments on several multi-view benchmark and multi-omics cancer data sets establish the effectiveness of the proposed method. The proposed work demonstrates how incorporation of Riemannanian optimization into the graph learning framework can boost multi-view clustering performance.

关键词： Manifolds Optimization Laplace equations Clustering algorithms Noise Geometry Perturbation methods graph learning matrix perturbation theory multi-view clustering Riemannian optimization spectral clustering

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graph learning from incomplete graph signals: From batch to online methods

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SIGNAL PROCESSING 2025年 226卷

作者： Zhang, Xiang Wang, Qiao Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Peoples R China Southeast Univ Sch Econ & Management Nanjing 210096 Peoples R China

Inferring graph topologies from data is crucial in many graph-related applications. Existing works typically assume that signals are observed at all nodes, which may not hold due to application-specific constraints. The problem becomes more challenging when data are sequentially available and no delay is tolerated. To address these issues, we propose an approach for learning graphs from incomplete data. First, the problem of learning graphs with missing data is formulated as maximizing the posterior distribution with hidden variables from a Bayesian perspective. Then, we propose an expectation maximization (EM) algorithm to solve the induced problem, in which graph learning and graph signal recovery are jointly performed. Furthermore, we extend the proposed EM algorithm to an online version to accommodate the delay-sensitive situations of sequential data. Theoretically, we analyze the dynamic regret of the proposed online algorithm, illustrating the effectiveness of our algorithm in tracking graphs from partial observations in an online manner. Finally, extensive experiments on synthetic and real data are conducted, and the results corroborate that our approach can learn graphs effectively from incomplete data in both batch and online situations.

关键词： graph learning Incomplete data graph signal recovery graph signal processing Online learning

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graph learning Over Polytopic Uncertain graph

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 716-720页

作者： Kishida, Masako Ono, Shunsuke Natl Inst Informat Principles Informat Res Div Tokyo 1018430 Japan Inst Sci Tokyo Dept Comp Sci Tokyo 1528550 Japan

This letter introduces a graph learning approach leveraging prior knowledge of graph topology. For this, we integrate the concept of polytopic uncertainty into existing approaches that learn graph Laplacians and adjacency matrices, constraining the solution space to a polytopic set. Our approach offers improved accuracy with reduced computational cost by focusing on a smaller solution space, effectively excluding implausible topologies. Numerical experiments demonstrate superior learned graph quality compared to existing approaches across various signal models and noise levels.

关键词： Laplace equations Topology Uncertainty Accuracy Optimization Computational efficiency Vectors Noise level Signal to noise ratio Matrix converters graph learning graph signal processing uncertainty

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HHgraphSum: Hierarchical heterogeneous graph learning for extractive document summarization

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DISPLAYS 2025年 86卷

作者： Hao, Pengyi Wu, Cunqi Bai, Cong Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou Peoples R China

Extractive summarization aims to select important sentences from the document to generate a summary. However, current extractive document summarization methods fail to fully consider the semantic information among sentences and the various relations in the entire document. Therefore, a novel end-to-end framework named hierarchical heterogeneous graph learning for document summarization (HHgraphSum) is proposed in this paper. In this framework, a hierarchical heterogeneous graph is constructed for the whole document, where the representation of sentences is learnt by several levels of graph neural network. The combination of single- direction message passing and bidirectional message passing helps graph learning obtain effective relations among sentences and words. For capturing the rich semantic information, space-time collaborative learning is designed to generate the primary features of sentences which are enhanced in graph learning. For generating a less redundant and more precise summary, a LSTM based predictor and a blocking strategy are explored. Evaluations both on a single-document dataset and a multi-document dataset demonstrate the effectiveness of the HHgraphSum. The code of HHgraphSum is available on Github:https://***/Devin100086/ HHgraphSum.

关键词： Extractive summarization Heterogeneous graph network graph learning

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Multi-Level Resource-Coherented graph learning for Website Fingerprinting Attacks

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2025年 20卷 693-708页

作者： Gao, Bo Liu, Weiwei Liu, Guangjie Nie, Fengyuan Huang, Jianan Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China Nanjing Univ Informat Sci & Technol Sch Elect & Informat Engn Nanjing 210044 Peoples R China Minist Educ Key Lab Intelligent Support Technol Complex Enviro Nanjing 210044 Peoples R China

Deep learning-based website fingerprinting (WF) attacks dominate website traffic classification. In the real world, the main challenges limiting their effectiveness are, on the one hand, the difficulty in countering the effect of content updates on the basis of accurate descriptions of page features in traffic representations. On the other hand, the model's accuracy relies on training numerous samples, requiring constant manual labeling. The key to solving the problem is to find a website traffic representation that can stably and accurately display page features, as well as to perform self-supervised learning that is not reliant on manual labeling. This study introduces the multi-level resource-coherented graph convolutional neural network (MRCGCN), a self-supervised learning-based WF attack. It analyzes website traffic using resources as the basic unit, which are coarser than packets, ensuring the page's unique resource layout while improving the robustness of the representations. Then, we utilized an echelon-ordered graph kernel function to extract the graph topology as the label for website traffic. Finally, a two-channel graph convolutional neural network is designed for constructing a self-supervised learning-based traffic classifier. We evaluated the WF attacks using real data in both closed- and open-world scenarios. The results demonstrate that the proposed WF attack has superior and more comprehensive performance compared to state-of-the-art methods.

关键词： Vectors Feature extraction Convolutional neural networks HTTP Uniform resource locators Accuracy Random forests Fingerprint recognition Support vector machines Servers Network traffic analysis website fingerprinting attacks graph learning representation learning self-supervised learning graph convolutional neural network

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Cancer-drug response prediction via feature aggregation and association graph learning

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 151卷

作者： Xu, Kaiyi Wang, Minhui Zou, Xin Ji, Chengfu Zhou, Hua Tang, Chang China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Nanjing Med Univ Dept Pharm Lianshui Peoples Hosp Kangda Coll Huaian 223300 Peoples R China Funing Peoples Hosp Dept Hematol Yancheng 224400 Peoples R China

Predicting cancer-drug response (CDR) not only guides the development of anticancer drugs but also provides personalized treatment plans for cancer patients, driving the advancement of precision medicine. Currently, numerous computational methods are utilized for CDR prediction and achieve great achievements. However, there are still some issues need to be addressed. From the aspect of data using, many studies primarily use single nucleotide variations (SNV) as features of cell lines, yet their acquisition from clinical data is often incomplete and inefficient, making it challenging to extend SNV-based CDR models from in vitro cell lines to clinical data. From the aspect of modeling, most works focus excessively on learning the intrinsic characteristics of drugs and cell lines, while neglecting to fully capture their interaction information and network-level knowledge. In this paper, we introduce a feature aggregation and association graph learning network (FA2GL) for CDR, which aggregates intrinsic and interaction feature representations of drugs and cell lines, and obtains their network-level knowledge from the association graph. In this network, FA2GL initially employs the self-attention block (SAB) to capture drug intrinsic features, with a focus on the relationships between substructures within the same drug, and utilizes the multilayer perceptron (MLP) to obtain cell line intrinsic features. Subsequently, it constructs the cross-attention block (CAB) to extract interaction information between drugs and cell lines, followed by the utilization of a fusion coefficient for aggregating these features. Additionally, FA2GL also constructs an association graph by introducing pipe nodes and similarity networks, bridging the feature gap between drugs and cell lines and enhancing predictions of similar substances, respectively. Then, a graph neural network (GNN) is applied to this graph to capture their network-level information. By integrating fine-grained and network-l

关键词： Cancer-drug response prediction Self-attention mechanism Multi-level feature fusion graph learning

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Anomaly Detection and Localization via graph learning

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ENERGIES 2025年第6期18卷 1475-1475页

作者： Amusan, Olabode Wu, Di North Dakota State Univ Dept Elect & Comp Engn Fargo ND 58105 USA

Phasor measurement units (PMUs) are being installed at an unprecedented rate on power systems, offering unique situation awareness capability. This paper presents a graph learning-based method for detecting and locating anomalies using PMU data. In this method, the graph learning technique is used to characterize the spatiotemporal relationship of distributed PMU data by constructing a spatiotemporal graph. Then, graph analysis is used to detect and locate anomalies by evaluating the global connectivity of spatiotemporal graphs at different times and the local connectivity of nodes in the relevant spatiotemporal graphs. The proposed method was verified using the IEEE-39 bus system and realistic PMU data. The method accurately identifies anomalies with an accuracy of 97% with a precision and recall of 80% and 100%, respectively. The results show the superiority and robustness of the proposed method as a powerful tool for detecting and locating anomalies using PMU data.

关键词： phasor measurement unit graph learning spatiotemporal relationship anomaly detection anomaly localization

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Distributed graph learning From Smooth Data: A Bayesian Framework

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2025年 73卷 1626-1642页

作者： Zhang, Jiayin Wu, Nan Zhang, Tingting Li, Bin Yan, Qinsiwei Ma, Xiaoli Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

The emerging field of graph learning, which aims to learn reasonable graph structures from data, plays a vital role in graph Signal Processing (GSP) and finds applications in various data processing domains. However, the existing approaches have primarily focused on learning deterministic graphs, and thus are not suitable for applications involving topological stochasticity, such as epidemiological models. In this paper, we develop a hierarchical Bayesian model for graph learning problem. Specifically, the generative model of smooth signals is formulated by transforming the graph topology into self-expressiveness coefficients and incorporating individual noise for each vertex. Tailored probability distributions are imposed on each edge to characterize the valid graph topology constraints along with edge-level probabilistic information. Building upon this, we derive the Bayesian graph learning (BGL) approach to efficiently estimate the graph structure in a distributed manner. In particular, based on the specific probabilistic dependencies, we derive a series of message passing rules by a mixture of Generalized Approximate Message Passing (GAMP) message and Belief Propagation (BP) message to iteratively approximate the posterior probabilities. Numerical experiments with both artificial and real data demonstrate that BGL learns more accurate graph structures and enhances machine learning tasks compared to state-of-the-art methods.

关键词： Probabilistic logic Topology Bayes methods Signal processing algorithms Laplace equations Data models Symmetric matrices Message passing Vectors Uncertainty graph learning smooth graph signal graph signal processing probabilistic model variational expectation maximization factor graph generalized approximate message passing

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Diversity and consistency graph learning guided multi-view unsupervised feature selection

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KNOWLEDGE-BASED SYSTEMS 2025年 317卷

作者： Xu, Hanxiao Xu, Da Zhang, Yusen Shandong Univ Sch Math & Stat Weihai 264200 Shandong Peoples R China Liaocheng Univ Sch Math Sci Liaocheng 252000 Shandong Peoples R China

Multi-view unsupervised feature selection has gained considerable attention lately because of its potential to enhance the performance of clustering and classification tasks, as well as improve the efficiency and interpretability of algorithms by selecting critical features from unlabeled heterogeneous data views. The existing feature selection algorithms based on graph learning mostly focus on directly using the initialized graphs to obtain a consensus graph. However, this ignores the potential cross-view diversity that may be induced by view-specific characteristics, corruptions, or noise. To address this issue, a novel and efficient algorithm is proposed that uses the Diversity and Consistency graph learning to guide Multi-view unsupervised Feature Selection (DCMFS). It is a unified optimization model that includes cross-view diversity and multi-view consistency graph learning, fusing the multiple consistent graphs to obtain a pure consensus graph, sparse feature selection, and learning optimal clustering structure graph. An efficient alternating iterative algorithm is designed to solve the optimization problem, and the convergence of the algorithm is verified through theoretical analysis and experimental results. Extensive experiments on eight benchmark datasets show the superiority and effectiveness of DCMFS compared to nine representative algorithms. DCMFS has been successfully applied to single-cell multi-omics data and identified many marker genes.

关键词： Diversity Consistency Multi-view unsupervised feature selection graph learning Single-cell multi-omics data

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