Abstract

Recent research indicates that the quantity of potentially dangerous software, or malware, is growing at an alarming rate. Malware can use a variety of techniques to hide its existence from the system. Device networks and internet connections must be protected from malware in order to stop it from infecting many machines. Many research on malware detection techniques have been conducted in the past few years. Malware detection is still challenging, though. When it comes to detecting malware that has previously been found, heuristic and signature-based methods are equally efficient and quick; however, signature-based methods have not shown to be very useful for recognising malware that has not yet been found. While there isn't a method that can identify every potential virus variation, deep learning algorithms can identify certain viruses, both new and old. However, behavior-based approaches are more successful in treating infections that are difficult to cure. This demonstrates how hard it is to design a viral screening programme that works and how much opportunity there is for creative ideas and methods. This paper provides a thorough examination of malware identification methods, encompassing more contemporary approaches that make use of tried-and-true methods.