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A Survey on Deep Learning Approaches for Malware Detection and Classification

Malware is a serious threat in the cybersecurity area because of its dynamic nature, the variety of malware families, stealth, propagation and the capability of evading traditional security products. Therefore, proper malware detection and classification are crucial for detecting malicious software and for securing com...

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HilEnT: Hilbert, Entropy Transformed Image Based Malware Detection

A novel malware binary to image transformation technique HilEnT is proposed based on a combination of Hilbert curve-based transformation of malware binary and the entropy feature comparison of malware file with benign and malware classes.

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This study presents a convolutional neural network (CNN) architecture based on transfer learning using EfficientNetB4 for multi-class image classification that improves classification accuracy while reducing training time and overfitting compared to conventional CNN models trained from scratch.

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MITHRIL: Malware Classification Through Lightweight cnn and Uniform Sampling

As the cybersecurity landscape continues to evolve, the relentless spread of sophisticated malware poses a substantial threat to the integrity and security of digital systems. A strategic approach to software analysis is malware visualization, a constantly evolving technique that seeks to transform the complex binary c...

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A Comparative Analysis of Machine Learning and Deep Learning Methods for Malware Detection in Portable Executable Files

Malware severely threatens cybersecurity through Portable Executable (PE) files. In this study, the performances of machine learning and deep learning models for malware detection are compared using static analysis features extracted from PE files. Tree-based ensemble models like Random Forest, XGBoost, LightGBM, and C...

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