A lightweight machine learning methods for malware classification

Today's Information Technology landscape is rapidly evolving. Cyber professionals are increasingly concerned about maintaining security and privacy. Research has shown that the emergence of new malware is on the rise. The realm of malware assault and defense is an endless circle. Antivirus firms are always striving to create signatures for hazardous malware, while attackers are constantly seeking to circumvent these signatures. Machine learning is incredibly successful at detecting malware. ML-based Malware detection falls into two categories: feature extraction and malware classification. The proposed solutions are designed specifically for low-power embedded devices and edge computing systems. These methods allow for real-time malware detection without imposing a significant computing burden. This study provides an in-depth analysis of feature reduction, and lightweight algorithms to enable this proposed method to work effectively and efficiently on any device starting from PC, IoT devices and servers. Extensive experiments were carried out on BODMAS dataset to provide the best low-complexity method with an F1 score of more than 99%.