Enhanced Malware Detection Using Grey Wolf Optimization and Deep Belief Neural Networks

Standard identification methods are flattering and less effective as attacks from malware get increasingly sophisticated. Considering current malware outbreaks employ tactics such as polymorphism, obfuscation and encryption, to avert identification, growing complicated approaches must be developed. This paper deals with a mixed model utilizing Deep Belief Neural Network (DBNN) for classifying and Grey Wolf Optimization (GWO) for choosing features. Whereas DBNN encodes complicated patterns by hierarchical learning, GWO optimizes the choosing of the more essential features, lowering the cost of computing and dataset complexity. Investigations reveal that the suggested GWO-DBNN model beats existing machine learning procedures in terms of detection accuracy, recall, precision, and false positive rate (FPR). These mixed tactics offer dependable and scalable solutions to the challenges faced by modern malware threats.