Hierarchical malware detection, family identification, and variant attribution using CNN-based hybrid models on grayscale executable images. [PDF]
Sci RepSaxena M, Das T.
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MaSS-Droid: Android Malware Detection Framework Using Multi-Layer Feature Screening and Stacking Integration. [PDF]
Entropy (Basel)Zhang Z, Han Q, Shi Z.
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Systematic Evaluation of Machine Learning and Deep Learning Models for IoT Malware Detection Across Ransomware, Rootkit, Spyware, Trojan, Botnet, Worm, Virus, and Keylogger. [PDF]
Sensors (Basel)Maghanaki M +3 more
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GCSA-ResNet: a deep neural network architecture for Malware detection. [PDF]
Sci RepFan Y +5 more
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AI-Driven Cybersecurity in IoT: Adaptive Malware Detection and Lightweight Encryption via TRIM-SEC Framework. [PDF]
Sensors (Basel)Mutambik I.
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MALITE: Lightweight Malware Detection and Classification for Constrained Devices. [PDF]
IEEE Trans Emerg Top ComputAnand S +5 more
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JDroid: Android malware detection using hybrid opcode feature vector. [PDF]
PeerJ Comput SciArslan RS.
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A malware detection method with function parameters encoding and function dependency modeling. [PDF]
PeerJ Comput SciHou R, Liu D, Jin X, Weng J, Geng G.
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GSIDroid: A Suspicious Subgraph-Driven and Interpretable Android Malware Detection System. [PDF]
Sensors (Basel)Huang H, Huang W, Jiang F.
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Logically explainable malware detection
Malware detection is a challenging application due to the rapid evolution of attack techniques, and traditional signature-based approaches struggle with the high volume of malware samples. Machine learning approaches face such limitation, but lack a clear interpretability, whereas interpretable models often underperform.
Anthony, Peter +6 more
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