A MODEL FOR DETECTING RANSOMWARE ATTACKS IN DIGITAL KIDNAPPING.

Idachaba Julius Adamu, Influence Ejirefe, Thomas Ali Gaga

Abstract


The dynamic concept of technology has caused an unprecedented technological and socio-economic development in everyday human activities. The fact is that there are an increasing number of digital attacks in digital kidnapping, purporting to be ransomware as a continuing threat and which 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 used; however, their reactive nature which has resulted in a continuing evolution and updating process. This is largely because detection mechanisms that can often be circumvented by introducing changes in the malicious code and its behaviour. In this paper we used classification techniques to develop a machine learning model for detecting and classification of ransomware as well as to increase the accuracy of detection and classification of ransomware. We train supervised machine learning algorithms for building the model and used the test set to performed the model evaluation with respect to confusion matrix to observe the model accuracy, of the proposed algorithm which enabling a systematic comparison of each algorithm. In this paper, supervised algorithms were used  namely naive Bayes resulted in an accuracy of 83.40% with the test set result, Decision Tree (J48) 97.60%, respectfully

Keywords


Digital Files, Ransomware, filtering, digital kidnaping attacks, machine learning model

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References


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