A Comprehensive Survey on Identification of Malware Types and Malware Classification Using Machine Learning Techniques

Malware is malicious code that has an effect on the user or device and allows an attacker to do significant harm to the machine. Malware is a kind of computer virus that increases in number and severity with each passing day, posing a major danger to the security of the Internet. This is a never-ending fight between security experts and malware producers, with the sophistication of malware increasing at the same rate as technological advancement. Current state-of-the-art research focuses on the development and use of machine learning methods for malware detection owing to the capacity of these techniques to stay up with malware evolution and keep up with the speed of technological advancement. The purpose of this study is to provide a systematic and comprehensive review of machine learning methods for malware detection, with a special emphasis on deep learning techniques, in order to aid in the identification of malware. The paper's primary contributions are (i) it provides a comprehensive description of the methods and features used in a traditional machine learning workflow for malware detection and classification; (ii) it examines the challenges and limitations of traditional machine learning; and (iii) it examines recent trends and progress in the field, with a particular emphasis on deep learning approaches. Furthermore, (iv) it addresses the research problems and unresolved obstacles associated with state-of-the-art methods, and (v) it discusses the future directions of study in the field. A better knowledge of malware detection and the new advances and research paths being explored by the scientific community to combat the issue is provided by the survey results, which aid researchers in their research efforts.