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.
View on arXiv@article{saini2025_2504.13408,
title={ OpCode-Based Malware Classification Using Machine Learning and Deep Learning Techniques },
author={ Varij Saini and Rudraksh Gupta and Neel Soni },
journal={arXiv preprint arXiv:2504.13408},
year={ 2025 }
}