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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Jingwen Zhang, Ru Liu, Jianyi Beijing Univ. of Posts & Telecom. Beijing China

ISBN: (纸本)9798350368741

Advanced persistent threats (APTs) pose significant risks to national infrastructure and corporate security. System logs record interactions between system entities, which are widely used for APT detection. However, the complex syntax and intricate relationships in system logs pose significant challenges to the performance of deep learning models. This paper proposes MM-LogVec, a novel anomaly detection method based on multimodal feature representation. It introduces three pre-training tasks to learn joint embeddings of logs and security events, establishing semantic associations between log operations and security events through cross-modal feature alignment, thereby enhancing the understanding of complex behaviors. Additionally, a lightweight graph autoencoder is used to model interaction relationships among entities, enabling entity-level anomaly detection via feature reconstruction. Evaluations across 10 APT scenarios show MM-LogVec's exceptional performance, with a true positive rate of 98.19% and a false positive rate of 3.93%, while improving processing speed by a factor of 9 compared to the state-of-the-art AIRTAG. © 2025 IEEE.

关键词： APT detection graph autoencoder log feature representation multimodal learning

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MinerFinder: A GAE-LSTM method for predicting location of miners in underground 22

MinerFinder: A GAE-LSTM method for predicting location of mi...

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30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL GIS)

作者： Goyal, Abhay Madria, Sanjay Frimpong, Samuel Missouri S&T Dept CS Rolla MO 65409 USA

ISBN: (纸本)9781450395298

Recent reports by the Mine Safety and Health Administration suggest that several injuries and fatalities could be attributed to the inability to accurately locate miners in case of disasters. Since underground mines have a complicated geometrical landscape and technological constraints such as no GPS information available, it is difficult to predict the location of a miner and hence may cause delays and inefficiencies in rescue operations during a disaster. A significant amount of research has been done to capture complex spatio-temporal relationships of movement of the nodes/people/things with time, spatial and temporal features to separately extract these relationships for location prediction. Although Markov Chains (MC) and Recurrent Neural Network (RNN) based methods have been used to predict locations, not all of them specifically mention the spatial locations, their connections and the aggregation techniques which would allow for the actual representations of the trajectory of miners. Addressing these concerns, we develop a first-of-its-kind end-to-end system entitled MinerFinder to predict the future location of the miners by incorporating Long Short Term Memory (LSTM) for trajectory information with graph autoencoder (GAE) for spatial environmental information representing the node connectivity. In addition, our approach will combine the miners' previous trajectories and daily repetitive patterns enhancing the prediction robustness. We evaluated MinerFinder over synthetic dataset to analyze the structure and location topology of an underground mine compared with foreground locations. Our model outperforms state of the art models and achieves an AP score ranging from (0.62 - 0.68) and Receiver Operating Characteristics (ROC) ranging from (0.63-0.68) with increasing percentage of prominent locations (most visited) to 50%.

关键词： graph autoencoder LSTM Location Prediction underground mine

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Adversarial random graph neural network for anomaly detection

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DIGITAL SIGNAL PROCESSING 2024年 146卷

作者： Tuzen, Ahmet Yaslan, Yusuf Aselsan Inc Ankara Turkiye Istanbul Tech Univ Istanbul Turkiye

Anomaly detection is distinguishing unusual objects from normal patterns. It is a complex task due to unpredictable nature of anomalies, which can appear in many forms or they can be hidden by mimicking normal behaviors in a graph structure. Such diversity makes this Deep learning approaches can solve these problems by extracting complex patterns from networks. However, addressing different forms of anomalous instances is essential for successfully implementing these approaches, as different anomaly types require further analysis. Additionally, it is challenging to interpret anomalies beforehand without focusing on every aspect of anomalies. Our objective is to propose an architecture capable of handling all types of anomalous entities by tackling challenges across various domains. In this paper, we introduce ARNAD, a novel framework that integrates three deep models to identify anomalies in graphs: graph neural network, autoencoder, and adversarial autoencoder. ARNAD approaches graph anomaly detection by utilizing the features of the deep parts, and four key elements stand out: (1) the autoencoder learns the overall graph structure and identifies highly deviated ones, (2) the graph neural network exploits graph structure to detect anomalies among the communities, (3) a fixed -size randomized neighborhood that prevents overfitting while reducing complexity (4) the adversarial autoencoder improves the robustness of the framework and discriminates anomalies. To detect anomalies, four receptive components assign risk scores to objects in the attributed network. We evaluated the framework with three synthetic datasets that simulate different behaviors of anomalies and six widely used real attributed networks. Our experimental results show that ARNAD performs competitively with other state-of-the-art models in detecting anomalous entities while minimizing false positives, demonstrating ARNAD's effectiveness in detecting graph anomalies.

关键词： Anomalous node detection graph neural network Random neighborhood graph autoencoder Adversarial autoencoder

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Deep multi-view graph clustering network with weighting mechanism and collaborative training

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 236卷

作者： Liu, Jing Cao, Fuyuan Jing, Xuechun Liang, Jiye Shanxi Univ Sch Comp & Informat Technol Key Lab Computat Intelligence & Chinese Informat P Minist Educ Taiyuan 030006 Peoples R China Shanxi Agr Univ Sch Software Taigu 030801 Peoples R China

With the development of graph convolutional network (GCN), which is powerful in graph embedding learning meanwhile can capture node feature information, deep multi-view graph clustering methods based on graph autoencoder have emerged as a new stream. Although they achieve satisfactory performance, they still have the following weaknesses: (1) some of them model the weights for different views in the encoder part by using attention in the multi-view embedding fusion layer, but fail to consider the weighting in the decoder part to measure the contributions of different views to the reconstruction loss. (2) Most of them directly conduct clustering on the multi-view common embedding layer, but fail to guarantee the alignment of clustering results of different views. To this end, we propose a novel GCN-based deep multi-view graph clustering network with weighting mechanism and collaborative training (DMVGC). The model is composed of multiple view-specific graph encoders and a unified graph decoder. Besides the specially-designed attention module in the encoder part, we construct a reconstruction loss with adaptive weighting mechanism in the decoder part. Additionally, a collaborative self-training clustering objective is jointly conducted on each view-specific embedding layer and the common embedding layer, to make the embedding of each view clustering-friendly toward a common partition. Experiments on several datasets demonstrate the effectiveness of our model.

关键词： Multi-view clustering graph clustering graph convolutional network graph autoencoder

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Multi-scale graph clustering network

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INFORMATION SCIENCES 2024年 678卷

作者： Li, Xiulai Wu, Wei Zhang, Bin Peng, Xin Zhejiang Univ Hainan Inst Sanya 572025 Peoples R China Hainan Univ Sch Cyberspace Secur Haikou 570228 Peoples R China Hainan Hairui Zhong Chuang Technol Co Ltd R&D Haikou 570228 Peoples R China

Deep graph clustering, a fundamental yet formidable task in data analysis, aims to partition samples belonging to the same category into their respective clusters. Recently, significant advancements in graph self -supervised learning have been made through generative and contrastive learning methods. However, existing methods focus on directly aggregating neighboring node information during the feature extraction stage, thereby neglecting the crucial long-range correlations between nodes. Consequently, non -neighbor node information within the same category remains unexplored, leading to subpar performance in the clustering task. To address this issue, we propose a generative method named Multi -scale graph Clustering Network (MGCN) to learn comprehensive and rich graph representations for deep graph clustering in the feature encoding stage. Specifically, we design a Multi -hop Adaptive Convolutional Module (MACM) integrated into MGCN, which effectively aggregates high -order neighbor node features in each layer of the network. Additionally, we develop an autoencoder to assist MACM in enhancing attribute information, which prevents the node's own features from being overshadowed in the multi -scale feature learning process. Experimental results demonstrate that our proposed MGCN method achieves significantly better clustering performance than existing methods on multiple public datasets.

关键词： graph clustering graph autoencoder High-order information Attention mechanism graph neural network

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Spatial-Spectral graph Contrastive Clustering With Hard Sample Mining for Hyperspectral Images

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷

作者： Guan, Renxiang Tu, Wenxuan Li, Zihao Yu, Hao Hu, Dayu Chen, Yuzeng Tang, Chang Yuan, Qiangqiang Liu, Xinwang Natl Univ Def Technol Coll Comp Changsha 410073 Peoples R China Hainan Univ Sch Comp Sci & Technol Haikou 570228 Hainan Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Wuhan Univ Sch Geodesy & Geomat Wuhan 430079 Peoples R China

Hyperspectral image (HSI) clustering is a fundamental yet challenging task that groups image pixels with similar features into distinct clusters. Among various approaches, contrastive learning methods, which employ the concept of encouraging semantically similar samples to move closer together while pushing semantically inconsistent samples apart, have garnered significant attention due to their promising performance. However, the most prevalent approaches face two major limitations: 1) treating all samples indiscriminately during optimization, where the abundance of well-categorized samples overwhelms the feature learning process and 2) tending to introduce noise when constructing positive sample pairs through view augmentation or searching the nearest neighbors, which would cause semantic drift of sample features. To solve these issues, we propose a graph autoencoder-based deep clustering framework named spatial-spectral graph contrastive clustering with hard sample mining (SSGCC) that constructs spatial-spectral dual views without data augmentation and focuses more on hard samples rather than treating all samples equally with the aid of spatial-spectral features. Concretely, we extract the spectral features and the neighborhood spatial features of the samples as dual branches to avoid the noise caused by data augmentation and develop the cluster-oriented consistency learning to facilitate the exchange of knowledge between the two spectral-spatial perspectives. In addition, we propose a hard sample mining-based contrastive learning scheme with the aid of spatial-spectral features. To better measure the importance of the samples, we combine spatial features and spectral features to calculate the similarity between sample pairs. The weights of hard sample pairs are dynamically up-weight while the easy ones are down-weighting to improve the discriminative capability. Extensive experiments on four benchmark HSI datasets demonstrate the effectiveness and superiority of

关键词： Contrastive learning deep clustering graph autoencoder hard sample mining hyperspectral images

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graph Representation Learning of Banking Transaction Network with EdgeWeight-Enhanced Attention and Textual Information 22

Graph Representation Learning of Banking Transaction Network...

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31st ACM Web Conference (WWW)

作者： Minakawa, Naoto Izumi, Kiyoshi Sakaji, Hiroki Sano, Hitomi Univ Tokyo Tokyo Japan

ISBN: (纸本)9781450391306

In this paper, we propose a novel approach to capture inter-company relationships from banking transaction data using graph neural networks with a special attention mechanism and textual industry or sector information. Transaction data owned by financial institutions can be an alternative source of information to comprehend real-time corporate activities. Such transaction data can be applied to predict stock price and miscellaneous macroeconomic indicators as well as to sophisticate credit and customer relationship management. Although the inter-company relationship is important, traditional methods for extracting information have not captured that enough. With the recent advances in deep learning on graphs, we can expect better extraction of inter-company information from banking transaction data. Especially, we analyze common issues that arise when we represent banking transactions as a network and propose an efficient solution to such problems by introducing a novel edge weight-enhanced attention mechanism, using textual information, and designing an efficient combination of existing graph neural networks.

关键词： graph Attention Network graph Isomorphism Network graph autoencoder Doc2Vec graph Representation Learning Natural Language Processing Financial Transaction Network

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BEDetector: A Two-Channel Encoding Method to Detect Vulnerabilities Based on Binary Similarity

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IEEE ACCESS 2021年 9卷 51631-51645页

作者： Yu, Lu Lu, Yuliang Shen, Yi Huang, Hui Zhu, Kailong Natl Univ Def Technol Coll Elect Engn Hefei 230007 Peoples R China Anhui Prov Key Lab Cyberspace Secur Situat Awaren Hefei 230007 Peoples R China

Applying neural network technology to binary similarity detection has become a promising search topic, and vulnerability detection is an important application field of binary similarity detection. When embedding binary code into matrix by neural network, the problem of feature representation also needs to be solved in vulnerability detection. However, most of the current researches extract the syntax or structural features of binary code, and take basic block as the minimum analysis unit, which is relatively coarse. In addition, the structural features of binary functions are usually represented by the dependency graph. In the embedding process, only the neighbour information of the node can be obtained, ignoring the global information of the graph. To solve these two problems, we propose a two-channel feature extraction method to obtain semantic feature in finer granularity and represent the structural features globally instead of locally. Inspired by natural language process, we propose a contextual semantic feature extraction method to obtain different granularity features of binary functions. It takes instruction as the minimum analysis unit and obtains the semantic relationship between instructions. Meanwhile, in order to represent the structural feature of each function, we propose a neural GAE model instead of the widely used structure2vec model. In this way, we can preserve and reconstruct the control dependencies between the basic blocks in the whole graph. We have implemented a prototype system BEDetector, evaluated the effectiveness of its neural model and compared the accuracy of vulnerability function detection with state-of-the-art system. Besides, we choose the real-world firmware files as the detection target and prove that BEDetector can achieve a relatively high detection rate. BEDetector could reach a precision of 88.8%, 86.7% and 100% when ranking top-50 candidate functions in the detection of the CVE vulnerability function ssl 3_get_key_exchange

关键词： Semantic analysis binary code embedding graph autoencoder binary similarity detection

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Learning style detection based on graph embedding enhancement 25

Learning style detection based on graph embedding enhancemen...

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Proceedings of the 2025 International Conference on Big Data and Informatization Education

作者： Jiayi Ma Zhiyuan Liu Business School Shandong Normal University Jinan Shandong China

ISBN: (纸本)9798400714405

In the “Internet and Education” era, accurately detecting students' learning styles is an indispensable part of personalized educational resource recommendations. Learning style is crucial for providing customized knowledge services and implementing effective learning guidance. This study aims to propose a student learning style detection method enhanced by graph embedding through modeling online learning data. Firstly, we analyze students' behavioral data and extract original behavioral features that reflect their learning styles. Then, we introduce a Bipartite graph autoencoder (BGAE) model to model student-learning resource interactions and extract graph embedding features. Finally, these features are then combined with original features to improve prediction performance. We conduct comparative experiments on multiple machine learning algorithms on a real dataset and ultimately determined the best performing random forest as the prediction model. The experimental results show that the classification model based on fused graph embedding features is significantly better in accuracy and stability than the model based solely on original features. The accuracy of the random forest is improved to 98.57%, which verifies the effectiveness of our research method and provides certain support for achieving personalized and precise recommendations in educational management.

关键词： graph autoencoder

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GRL-LS: A learning style detection in online education using graph representation learning

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 201卷 1页

作者： Muhammad, Bello Ahmad Qi, Chao Wu, Zhenqiang Ahmad, Hafsa Kabir Minist Educ Key Lab Modern Teaching Technol Xian 710062 Shaanxi Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian 710062 Peoples R China Bayero Univ Kano Kano 700241 Nigeria

The accessibility and popularity of online learning have aided the spread of modern learning systems, which provide numerous opportunities for studying the behavior of learners and improving their learning quality. In online platforms, different learners have different learning styles based on their learning behavior. As a result, analyzing learners' behavior and detecting their learning styles is important in order to provide appropriate resource recommendations, thereby improving their learning outcomes. While several approaches for detecting learning styles have been suggested, behavioral data is collected on a large scale, making it difficult to capture learners' behavior efficiently. Furthermore, current approaches neglect relationships between learner-resource interaction and use techniques based on individual learners' features. However, considering these relations can provide additional benefits. Motivated by these limitations, we propose GRL-LS, a learning style detection approach based on graph representation learning techniques. We first construct a bipartite graph representing the interaction between the set of learners and the set of learning resources. We then introduce a graph embedding technique that learns the latent representations of learners and resources. The learned representation is mapped with the Felder-Silverman learning style model (FSLSM) to identify and group learners using K-means algorithms. The GRL-LS technique can be used in a wide range of educational settings and can be customized to fit a variety of learning style models. An experiment on real-world KDDCup datasets was conducted to evaluate the effectiveness of the proposed GRL-LS approach. The results show that the proposed GRL-LS is efficient at detecting learning style, performs best for all FSLSM dimensions with average accuracy and precision of 88.25% and 78.50% respectively, and outperforms existing approaches with an average improvement of 8.55% accuracy and 2.33% precision

关键词： graph learning graph autoencoder Learning style Interactive learning environment Learners behavior

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