Malware detection and classification: A comprehensive review

t. One of the most vital issues in today’s era of internet and data exchange is malware. Malicious activities are on their peak in recent years and have gone beyond simple hacking machines and spreading viruses. Every day, new types of malware with increased complexity are being generated which can cause major threats to computer systems if left unintended. The level of penetration of malware into information systems also varies. Thus, the process of detecting malware becomes more important when there is a threat to the security of confidential information. Malware detection becomes really a challenge in cyber security. In this paper, we review the different approaches used for malware detection and classification. Methods of malware detection and categorization are discussed, including static analysis, dynamic analysis, and hybrid analysis. In addition, the researchers examined many machine learning techniques currently in use for finding and categorising malware. Insights into the current state of the art in malware detection and classification are provided in this paper, along with recommendations for future research to detect polymorphic and metamorphic malware and address data imbalance in machine learning-based approaches.