Application of Machine Learning Models for Malware Classification With Real and Synthetic Datasets

Santosh Joshi, Alexander Perez Pons, Shrirang Ambaji Kulkarni and Himanshu Upadhyay
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Santosh Joshi: Florida International University, USA
Alexander Perez Pons: Florida International University, USA
Shrirang Ambaji Kulkarni: Manipal Institute of Technology, Bengaluru, India
Himanshu Upadhyay: Florida International University, USA

International Journal of Information Security and Privacy (IJISP), 2024, vol. 18, issue 1, 1-23

Abstract: Stacking of multiple Machine Learning (ML) classifiers have gained popularity in addressing anomalous data classification along with Deep Learning (DL) algorithms. This study compares traditional ML classifiers, multi-layer stacking ML classifiers, and DL classifiers using an open-source malware dataset-containing equal numbers of benign and malware samples. The results on the realistic dataset indicate that the DL classifier, utilizing a Bidirectional Long Short-Term Memory (BiLSTM) model, outperformed the stacked classifiers with Logistic Regression (LR) and Support Vector Machine (SVM) as Meta learners by 36.78% and 39.69%, respectively, in terms of classification accuracy and performance. The research work was extended to study the impact of Generative Adversarial Network (GAN) based synthetic dataset of relatively smaller size on deep learning models. It was observed that the Deep Learning Multi-Layer Perceptron (DLMLP) Model had relatively superior performance as compared to complex deep learning models like Long Short-Term Memory LSTM and BiLSTM

Date: 2024
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