An Extensive Survey on Malware Detection and Prevention Mechanisms Using Advanced Technologies

he exponential growth in malware attacks, especially ransomware, is a critical challenge to digital infrastructure and global cybersecurity. Conventional signature-based detection techniques are less effective against sophisticated polymorphic and metamorphic malware. This paper offers a comprehensive survey of malware detection methods, with emphasis on Behavioral signature-based detection. It examines state-of-the-art technologies, such as dynamic analysis, machine learning, deep learning, and generative adversarial networks (GANs), and compares them in terms of their efficacy in detecting malware based on behavior patterns instead of static code. It draws from 46 peer-reviewed papers and emphasizes key findings, detection architectures, and innovations like RansomNet, DeepCodeLock, and PlausMal-GAN. The review ends by summarizing current limitations, issues such as Behavioral drift, and directions for the future in hybrid and intelligent malware detection systems