Persistent graph stream summarization for real-time graph analytics

Jia, Yan; Gu, Zhaoquan; Jiang, Zhihao; Gao, Cuiyun; Yang, Jianye

doi:10.1007/s11280-023-01165-z

Cited by 28 publications

(3 citation statements)

References 34 publications

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“…With many important breakthroughs in the application of deep neural network models to graphs, graph-based methods have attracted more and more attention from scholars in many research areas. For example, to better analyze large-volume and fast-changing streaming graphs, Gu and Jiang, et al, proposed a method to efficiently find the graph stream summary, PGSS-MDC [20], to address the problem of providing highly accurate and fast answers to graph queries within a certain time interval. Meanwhile, the use of graph-based methods to detect network intrusions have also become an important research direction; for example, to to detect the existence of multi-step attacks in a large number of security alarms in smart city application scenarios, Jia, et al, proposed the ACAM network security analysis framework [21], which is a network security analysis framework based on the Knowledge Representation Model (Multi-dimensional Data Association and Intelligent Analysis Model [22]).…”

Section: Related Workmentioning

confidence: 99%

AGCN-Domain: Detecting Malicious Domains with Graph Convolutional Network and Attention Mechanism

Luo,

Li,

Cheng

et al. 2024

Mathematics

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Domain Name System (DNS) plays an infrastructure role in providing the directory service for mapping domains to IPs on the Internet. Considering the foundation and openness of DNS, it is not surprising that adversaries register massive domains to enable multiple malicious activities, such as spam, command and control (C&C), malware distribution, click fraud, etc. Therefore, detecting malicious domains is a significant topic in security research. Although a substantial quantity of research has been conducted, previous work has failed to fuse multiple relationship features to uncover the deep underlying relationships between domains, thus largely limiting their level of performance. In this paper, we proposed AGCN-Domain to detect malicious domains by combining various relations. The core concept behind our work is to analyze relations between domains according to their behaviors in multiple perspectives and fuse them intelligently. The AGCN-Domain model utilizes three relationships (client relation, resolution relation, and cname relation) to construct three relationship feature graphs to extract features and intelligently fuse the features extracted from the graphs through an attention mechanism. After the relationship features are extracted from the domain names, they are put into the trained classifier to be processed. Through our experiments, we have demonstrated the performance of our proposed AGCN-Domain model. With 10% initialized labels in the dataset, our AGCN-Domain model achieved an accuracy of 94.27% and the F1 score of 87.93%, significantly outperforming other methods in the comparative experiments.

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Section: Related Workmentioning

confidence: 99%

AGCN-Domain: Detecting Malicious Domains with Graph Convolutional Network and Attention Mechanism

Luo,

Li,

Cheng

et al. 2024

Mathematics

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“…In terms of the definition of hyperedges and edges, graphs are a special case of hypergraphs. There is a large amount of discrete data with complex relationships in the real world, but modeling binary relationships between discrete data via graphs can result in a loss of information, whereas hypergraphs are a more direct and natural way of modeling [23]. Figure 1 shows the topology difference between graphs and hypergraphs.…”

Section: Preliminariesmentioning

confidence: 99%

Android Malware Detection Based on Hypergraph Neural Networks

Zhang,

Wu,

et al. 2023

Applied Sciences

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Android has been the most widely used operating system for mobile phones over the past few years. Malicious attacks against android are a major privacy and security concern. Malware detection techniques for android applications are therefore significant. A class of methods using Function Call Graphs (FCGs) for android malware detection has shown great potential. The relationships between functions are limited to simple binary relationships (i.e., graphs) in these methods. However, one function often calls several other functions to produce specific effects in android applications, which cannot be captured with FCGs. In this paper, we propose to formalize android malware detection as a hypergraph-level classification task. A hypergraph is a topology capable of portraying complex relationships between multiple vertices, which can better characterize the functional behavior of android applications. We model android applications using hypergraphs and extract the embedded features of android applications using hypergraph neural networks to represent the functional behavior of android applications. Hypergraph neural networks can encode high-order data correlation in a hypergraph structure for data representation learning. In experiments, we validate the gaining effect of hypergraphs on detection performance across two open-source android application datasets. Especially, HGNNP obtains the best classification performance of 91.10% on the Malnet-Tiny dataset and 97.1% on the Drebin dataset, which outperforms all baseline methods.

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“…While knowledge graph can represent facts clearly [8], most of these facts are static and not easily changed. In the real world, dynamic facts typically dominate and may be distorted by the passage of time and changes in space.…”

Section: Introductionmentioning

confidence: 99%

TSEE: a novel knowledge embedding framework for cyberspace security

Zhao,

Gu,

Jia

et al. 2023

World Wide Web

Self Cite

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Knowledge representation models have been extensively studied and they provide an important foundation for artificial intelligence. However, the existing knowledge representation models or related knowledge embedding methods mostly aim at static or temporal knowledge, which are not suitable for highly spatio-temporal relevant knowledge, such as the cyber security knowledge. In this paper, we propose a knowledge embedding framework called TSEE to handle this problem, which builds on the MDATA model to represent and utilize dynamic knowledge for cyber security. TSEE is composed of knowledge extraction module, knowledge representation module, knowledge embedding module, and situational awareness module. There modules can obtain, transform, and embed cyber security knowledge from different sources, improving the detection capabilities of various complicated attacks. We conduct experiments on the cyber range for evaluation, and the experimental results validate the higher prediction accuracy and stronger extendability than existing embedding methods. The framework can effectively improve the cyber security defense capabilities in the future.

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Persistent graph stream summarization for real-time graph analytics

Cited by 28 publications

References 34 publications

AGCN-Domain: Detecting Malicious Domains with Graph Convolutional Network and Attention Mechanism

AGCN-Domain: Detecting Malicious Domains with Graph Convolutional Network and Attention Mechanism

Android Malware Detection Based on Hypergraph Neural Networks

TSEE: a novel knowledge embedding framework for cyberspace security

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