A Literature Review of Various Analysis Methods and Classification techniques of Malware

Malware disrupts the natural behaviour of computer systems, hinders performance, and may cause a significant loss to the computer system owner. The growth or advancement in the number of malware variants has necessitated the requirement of advanced techniques for the detection, identification, and classification of malware. The hybrid approach is predominantly employed since static and dynamic analysis methods have drawbacks and are time-consuming. Moreover, recent malware variants use obfuscation techniques and exhibit polymorphic and metamorphic behaviour. It was noticed that even though classical machine learning methods gave better performance and quicker classification, they suffered from the problem of misclassification. Newer approaches such as image processing techniques and deep learning architectures are thus employed. The paper focuses on the survey of various detection, identification, and classification methods of malware and is an effort to put forward the best approach.