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Mal2GCN: a robust malware detection approach using deep graph convolutional networks with non-negative weights

Research article (Journal of Computer Virology and Hacking Techniques, 2023) · cited 16× · AI/ML
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Mal2GCN: a robust malware detection approach using deep graph convolutional networks with non-negative weights

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Mal2GCN: a robust malware detection approach using deep graph convolutional networks with non-negative weights is a scholarly article[1].

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  • Mal2GCN: a robust malware detection approach using deep graph convolutional networks with non-negative weights's instance of is recorded as scholarly article[2].

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Rasool Jalili

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APA 4ort.xyz Knowledge Graph. (2026). Mal2GCN: a robust malware detection approach using deep graph convolutional networks with non-negative weights. Retrieved May 24, 2026, from https://4ort.xyz/entity/mal2gcn-a-robust-malware-detection-approach-using-deep-graph-convolutional-networks-with-non-negative-weights
MLA “Mal2GCN: a robust malware detection approach using deep graph convolutional networks with non-negative weights.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/mal2gcn-a-robust-malware-detection-approach-using-deep-graph-convolutional-networks-with-non-negative-weights.
BibTeX @misc{4ortxyz_mal2gcn-a-robust-malware-detection-approach-using-deep-graph-convolutional-networks-with-non-negative-weights_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Mal2GCN: a robust malware detection approach using deep graph convolutional networks with non-negative weights}}, year = {2026}, url = {https://4ort.xyz/entity/mal2gcn-a-robust-malware-detection-approach-using-deep-graph-convolutional-networks-with-non-negative-weights}, note = {Accessed: 2026-05-24}}
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