Proactive zero-day malware evolution forecasting via self-supervised multi-view contrastive representation learning and hybrid generative modeling
TL;DR
A multiple-view self-supervised malware evolution forecasting framework that shifts focus from merely detecting malware to proactively anticipating future threats, showing improved detection performance, better zero-day/out-of-distribution generalization, more realistic and behaviorally consistent generated samples, and enhanced accuracy in predicting future malware trajectories is introduced.
Abstract
Zero-day malware continues to pose a significant challenge in cybersecurity, primarily due to the rapid development of polymorphic and metamorphic variants that evade traditional detection methods based on signatures or machine learning (ML). Most existing ML approaches are reactive, identifying specific malware families and relying on extensive labeling, making them ineffective against novel malware types. This paper introduces a multiple-view self-supervised malware evolution forecasting framework that shifts focus from merely detecting malware to proactively anticipating future threats. The framework encompasses numerous sources of evidence (for example, opcodes; structural metadata; entropy-graphs and API Calls). Each distinct layer of evidence is processed using a different deep learning encoder. The deep-embedded representations generated by each encoder are combined within a common latent space through cross-view contrastive training to facilitate predictive analysis on future malware samples. This represents the evolutionary trajectory of malware over time, allowing for predictive modeling of future malware samples based on sequences of mutations. Furthermore, the framework employs a hybrid generative model utilizing variational autoencoder regularization in conjunction with diffusion-based GANs to create realistic future malware samples. These samples are then ranked according to novelty, similarity, and severity to develop a risk index aimed at enabling proactive signature generation. Experimental evaluations on publicly available datasets of Android and Windows malware demonstrate that the proposed approach outperforms baseline algorithms including classical ML, deep learning, GAN augmentation, and transformer-based methods—showing improved detection performance, better zero-day/out-of-distribution generalization, more realistic and behaviorally consistent generated samples, and enhanced accuracy in predicting future malware trajectories. This framework paves the way for proactive defense strategies against evolving malware in cybersecurity operations.