Intelligence Malware Detection System with LinearSVC

Malware, a form of malicious software, is engineered to disrupt computer functionality, exfiltrate sensitive information, surveil user activity, and perpetrate other illicit malicious actions. It continues to proliferate daily. This escalation poses significant challenges to the field of malware analysis and detection. Consequently, the development of automated malware classification systems becomes imperative. Machine learning emerges as a promising approach for this purpose. This research proposes a malware classification system employing the Support Vector Machine (SVM) algorithm, with a Linear SVC approach, evaluated using the EMBER dataset. The proposed system achieves the highest accuracy of 98.9% with a total time of 59 seconds.