Trojan Detection System Using Machine Learning Approach

Malware attack cases continue to rise in our current day. The Trojan attack, which may be extremely destructive by unlawfully controlling other users' computers order steal their data. As a result, horse detection is essential identify the and limit attacks. In this study, we proposed system that employed machine learning algorithms detect horses within system. A public dataset of contain 2001 samples comprises 1041 960 benign used train classification. paper, trained using four types classifiers are Random Forest, J48, Decision Table Naïve Bayes. WEKA for execution classification process performance analysis. results indicated with Forest obtained maximum level accuracy.