A MACHINE LEARNING MODEL FOR DETECTING AND CLASSIFICATION OF RANSOMWARE

Abstract

The dynamic concept of technology has caused an unprecedented technological and socio-economic development in everyday human activities. The fact is that there is an increasing number of digital attacks and digital kidnapping, purporting to be ransomware as a continuing threat. This has resulted in the battle between the development and detection of new techniques. Detection and mitigation systems have been developed and are in wide-scale use. However, their reactive nature has resulted in a continuing evolution and updating process. This is largely because detection mechanisms can often be circumvented by introducing changes in the malicious code and its behavior. In this paper, classification techniques were used to develop a machine learning model for the detection and classification of ransomware. This will also increase the accuracy of the detection and classification of ransomware. Supervised machine learning algorithms were trained for building the model and the test set used to perform the model evaluation using confusion matrix. This ensured a systematic comparison of each algorithm. The supervised algorithms used are naive Bayes and decision tree (J48). This resulted to an accuracy of 83.40% for Naïve Bayes and 97.60% for Decision Tree (J48).The Research also determine sensitivity and specificity.