OpCode-Based Malware Classification Using Machine Learning and Deep Learning Techniques

Varij Saini, Rudraksh Gupta, Neel Soni
4/18/2025
cs.CRcs.LG

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Abstract

This technical report presents a comprehensive analysis of malware classification using OpCode sequences. Two distinct approaches are evaluated: traditional machine learning using n-gram analysis with Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Tree classifiers; and a deep learning approach employing a Convolutional Neural Network (CNN). The traditional machine learning approach establishes a baseline using handcrafted 1-gram and 2-gram features from disassembled malware samples. The deep learning methodology builds upon the work proposed in "Deep Android Malware Detection" by McLaughlin et al. and evaluates the performance of a CNN model trained to automatically extract features from raw OpCode data. Empirical results are compared using standard performance metrics (accuracy, precision, recall, and F1-score). While the SVM classifier outperforms other traditional techniques, the CNN model demonstrates competitive performance with the added benefit of automated feature extraction.

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Cite this paper

@article{saini2025opcodebased,
  title  = {OpCode-Based Malware Classification Using Machine Learning and Deep Learning Techniques},
  author = {Varij Saini and Rudraksh Gupta and Neel Soni},
  year   = {2025},
  eprint = {2504.13408},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2504.13408v1}
}

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