Publication Date

6-30-2026

Document Type

Article

Publication Title

Electronics Switzerland

Volume

15

Issue

13

DOI

10.3390/electronics15132848

Abstract

Deep learning-based malware classification using image representations has emerged as a highly effective paradigm for threat detection. However, training robust neural networks is frequently bottlenecked by data scarcity and severe class imbalances in real-world repositories. This study investigates the viability of using an autoregressive PixelCNN framework to synthesize high-fidelity, class-specific malware images to augment limited training distributions. Utilizing the benchmark Malimg dataset, we systematically evaluate a Convolutional Neural Network (CNN) classifier across varying ratios of synthetic-to-authentic data under strict data scarcity constraints (ranging from 10 to 80 authentic samples per family). Our experimental results reveal that while PixelCNN successfully replicates intricate, byte-level micro-textures, classifiers trained exclusively on synthetic data experience catastrophic performance degradation, yielding an accuracy of just 3%. Crucially, however, the introduction of a minimal authentic data anchor (15% to 20%) restores functional decision boundaries, immediately elevating classification accuracy up to 72%. Furthermore, performance saturates rapidly once the training matrix reaches a 50/50 synthetic-to-authentic split, achieving up to 82% classification accuracy, rendering it highly competitive with the 89% accuracy upper bound of a fully authentic baseline. These findings demonstrate an exceptional degree of data efficiency, proving that generative autoregressive augmentation can halve the authentic data collection burden in cybersecurity workflows provided a minor, real-world baseline anchor is preserved.

Keywords

Convolutional Neural Network, deep learning, malware classification, PixelCNN, synthetic data generation

Comments

This article belongs to the Special Issue AI in Cybersecurity, 3rd Edition

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Department

Computer Science

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