The latest research and theoretical studies have shown that malware is among the biggest dangers to our digital environment. The methods for removing malware have advanced in recent years to provide protection. In the past, a variety of traditional methods were employed to identify malware with different characteristics such as signatures, heuristics as well as other. Methods for detecting malware that were traditional in nature could not defeat the modern malware types as well as their advanced obfuscation strategies. Deep Learning outperforms other conventional techniques in malware type detection. Additionally, these techniques offer quick malware detection and have excellent rate of detection as well as the analysis of various malware types. The study of recently introduced Deep Learning-based malware prevention techniques and the evolution of their technology is relevant to this research. The study offers an in-depth review of recently created algorithms for detecting malware using DL. In addition, new malware types are studied, and detection strategies for malwares in Mobile (Android and iOS), Windows, IoT Advanced Persistent Threats (APTs) as well as Ransomware are thoroughly reviewed.
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