Deep Learning for Zero-Day Flash Malware Detection: Prospective and Challenges

Abstract

The rise of zero-day Flash malware has introduced significant security challenges due to its ability to exploit previously unknown vulnerabilities and evade traditional detection systems. This paper presents a novel deep learning-based approach leveraging a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model to detect zero-day Flash malware effectively. Unlike conventional signature-based or heuristic detection mechanisms, our method automatically extracts and learns both spatial and temporal features from Flash file samples to improve detection accuracy and resilience against evasion techniques. The model was trained and evaluated on a robust, diversified dataset consisting of benign and malicious Flash samples, demonstrating superior performance compared to existing methods. Performance evaluation was conducted using precision, recall, F1-score, and ROC-AUC metrics. The experimental results show a detection accuracy of 97.5%, with a significantly reduced false positive rate. This study highlights the potential of deep learning, especially hybrid architectures, in addressing the evolving threat of zero-day malware. It further opens new avenues for real-time, intelligent malware detection systems applicable in broader cybersecurity contexts.