Feluca: A Two-Stage Graph Coloring Algorithm With Color-Centric Paradigm on GPU

Zheng, Zhigao; Shi, Xuanhua; He, Ligang; Jin, Hai; Wei, Shuo; Dai, Hulin; Peng, Xuan

doi:10.1109/tpds.2020.3014173

Cited by 10 publications

(2 citation statements)

References 49 publications

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“…Graph neural networks are commonly used for modeling relationships between entities and have been rapidly developed in recent years in both theoretical [7][8][9][10]50] and application areas [11,12,52,53]. Based on the spectral and spatial domains, graph neural networks can be categorized into two groups, where common spectral GNNs include SCNN [13] and GCNs [14], and com-mon spatial methods include GraphSAGE [15], GAT [8] and JK-Net [16].…”

Section: Introductionmentioning

confidence: 99%

MalDMTP: A Multi-tier Pooling Method for Malware Detection based on Graph Classification

Kou,

Qiu,

Wang

et al. 2024

Preprint

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With the development and adoption of cloud platforms in various fields, malware attacks have become a serious threat to the Internet cloud ecosystem. However, the pooling process of existing graph classification techniques for malware variant detection uses only a serial and single strategy, resulting in localized malicious behaviors of malware that may be overlooked. In this paper, we propose MalDMTP, a malware detection framework based on multilevel graph classification learning, which implements the graph pooling process for malware classification in parallel and performs graph instance-based discrimination. In particular, MalDMTP first constructs an API call graph based on results obtained from dynamic execution of malware. Then it combines multiple graph neural network learning strategies through multi-level pooling to learn the global importance of nodes in the pooled graph and extract node representations from multiple perspectives for heterogeneous graphs. After that, MalDMTP is aggregated into graph representations by the graph-level pooling function GMT based on a multi-head attention mechanism, which goes through a classifier in order to obtain malware prediction labels. Experimental results show that the proposed MalDMTP can achieve 96.53\% accuracy on the Alibaba cloud malware dataset, which improves 1.9\% ~ 7.6\% over the previous single-graph pooling methods on the graph classification task of malware detection.

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Section: Introductionmentioning

confidence: 99%

MalDMTP: A Multi-tier Pooling Method for Malware Detection based on Graph Classification

Kou,

Qiu,

Wang

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

MalDMTP: A Multi-tier Pooling Method for Malware Detection based on Graph Classification

Kou,

Qiu,

Wang

et al. 2024

Mobile Netw Appl

View full text Add to dashboard Cite

With the development and adoption of cloud platforms in various fields, malware attacks have become a serious threat to the Internet cloud ecosystem. However, the pooling process of existing graph classification techniques for malware variant detection uses only a serial and single strategy, resulting in localized malicious behaviors of malware that may be overlooked. In this paper, we propose MalDMTP, a malware detection framework based on multilevel graph classification learning, which implements the graph pooling process for malware classification in parallel and performs graph instancebased discrimination. In particular, MalDMTP first constructs an API call graph based on results obtained from dynamic execution of malware. Then it combines multiple graph neural network learning strategies through multilevel pooling to learn the global importance of nodes in the pooled graph and extract node representations from multiple perspectives for heterogeneous graphs. After that, MalDMTP is aggregated into graph representations by the graph-level pooling function GMT based on a multi-head attention mechanism, which goes through a classifier in order to obtain malware prediction labels. Experimental results show that the proposed MalDMTP can achieve 96.53% accuracy on the Alibaba cloud malware dataset, which improves 1.9% 7.6% over the previous single-graph pooling methods on the graph classification task of malware detection.

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Identification of Key Nodes in Wireless Communication Networks

Chen,

Wang

2024

Lecture Notes in Electrical Engineering

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Feluca: A Two-Stage Graph Coloring Algorithm With Color-Centric Paradigm on GPU

Abstract: Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.

Cited by 10 publications

References 49 publications

MalDMTP: A Multi-tier Pooling Method for Malware Detection based on Graph Classification

MalDMTP: A Multi-tier Pooling Method for Malware Detection based on Graph Classification

MalDMTP: A Multi-tier Pooling Method for Malware Detection based on Graph Classification

Identification of Key Nodes in Wireless Communication Networks

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