Efficient malware detection in software systems using handcrafted features, Bi-GRUs, and VAEs

With the increasingly complex cyber-attacks, malware detection is now crucially required, which poses difficult challenges to traditional security systems regarding software testing. Existing techniques such as SVR, LSTM, and HMM are not capable of malware detection, especially for multiple unknown threats and large datasets. These often lead to high false positive rates, slow detection times, and limited capabilities for anomalous detection. The aim to bridge the said gaps is what this paper proposes a hybrid malware detection framework based on handcrafted feature extraction using LightGBM, temporal analysis with Bidirectional Gated Recurrent Units (Bi-GRU), and anomaly detection with Variational Autoencoders (VAE), fused with attention mechanism for improved performance. The novelty of this approach lies in its ability to bring on board an innovative combination of techniques that capture a wide variety of attack behaviors, so the system provides robust detection significantly improved in terms of detection accuracy as well as efficiency. The proposed framework attains 99.8% accuracy, 99.2% precision, 98.5% recall, and a considerable decrease in detection time down to 0.08 seconds, along with low false positive rates of 0.8% and false negative rates of 0.3%. Superior performance is demonstrated in comparison with the existing methods, offering faster and more precise malware detection. This is one great way to ascertain the breakthrough in malware detection, boosting system security and performance for software testing, and has the innate capacity to be scalable and adaptable to more complex and evolving threats in whatever network environments to ensure even faster, accurate detection and mitigation.