The proliferation of malicious software across digital ecosystems has necessitated the development of advanced detection mechanisms capable of identifying increasingly sophisticated threats. Traditional signature-based approaches have proven inadequate in addressing polymorphic and zero-day attacks, thereby driving the adoption of machine learning and deep learning methodologies in cybersecurity. This study investigates a hybrid framework for the identification of harmful programs through the integration of deep feature extraction networks and context-aware sequential modeling techniques.

The research synthesizes theoretical foundations from neural network-based classification, autoencoder-driven representation learning, and sequence modeling approaches such as recurrent neural networks and transformer architectures. By leveraging deep feature extraction mechanisms, the framework captures high-dimensional representations of executable patterns, while context-aware sequential modeling enhances the system’s ability to interpret temporal and structural dependencies within code and behavioral data.

The study adopts a conceptual-analytical methodology grounded in recent advancements in malware detection, intrusion prevention, and adversarial resilience. The integration of hybrid models enables improved classification accuracy, particularly in environments characterized by dynamic threat evolution. Furthermore, the framework incorporates feature selection, anomaly detection, and optimization strategies to enhance computational efficiency and scalability.

Findings suggest that the fusion of convolutional neural architectures, autoencoder-based encoding, and sequential learning models significantly improves detection performance compared to standalone approaches. The hybrid model demonstrates enhanced capability in identifying obfuscated malware, ransomware variants, and network-based intrusion patterns. However, challenges related to model interpretability, data heterogeneity, and adversarial manipulation remain critical concerns.

This research contributes to the cybersecurity domain by proposing a unified modeling paradigm that aligns deep feature extraction with contextual sequence learning. The study provides theoretical and practical insights for developing robust, adaptive, and scalable malware detection systems, thereby supporting the advancement of intelligent cybersecurity frameworks.