Ransomware Detection Using Binary Classification
Kazi Samiul Kader, Md Tareque Hasan Tahsin, Md. Shohrab Hossain, Husnu S. Narman · 2021 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · 2021
Nowadays ransomware attack is one of the most widely used tactics for cyber attacks. It is computationally infeasible to revert the damage done by a ransomware attack. Therefore, it is of utmost importance to identify a program to be ransomware during installation time. In this paper, machine learning binary classification algorithms have been used to identify ransomware through dynamic analysis of several features of ransomware. At first, manual selection of features is analyzed, and later on, we have used the automatic feature selection process using the K best algorithm. Results show that in both cases (manual and automatic selection), we achieved a significant percentage of accuracy to detect ransomware at runtime.