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Development of a Machine Learning Model for Detecting and Classifying Ransomware

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dc.contributor.author Asaju C.B.
dc.contributor.author Otoo-Arthur D.
dc.contributor.author Orah R.O.
dc.contributor.author Sekyi-Dadson F.
dc.date.accessioned 2022-10-31T15:05:15Z
dc.date.available 2022-10-31T15:05:15Z
dc.date.issued 2021
dc.identifier.other 10.1109/ICMEAS52683.2021.9692402
dc.identifier.uri http://41.74.91.244:8080/handle/123456789/332
dc.description Asaju, C.B., Federal Polytechnic, Department of Computer Science, Kogi State, Nigeria; Otoo-Arthur, D., University of Education, Department of Ict Education, Winneba, Ghana; Orah, R.O., Salem University, Department of Computer Science, Kogi State, Lokoja, Nigeria; Sekyi-Dadson, F., Kibi Presbyterian College of Education, Department of Mathematics Ict, Kibi, Ghana en_US
dc.description.abstract The dynamic concept of technology has caused unprecedented technological and socio-economic development in everyday human activities. The fact is that there is an increasing number of digital attacks in digital kidnapping, purporting to be ransomware as a continuing threat, resulting 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 mainly because detection mechanisms can often be circumvented by introducing changes in the malicious code and its behaviour. In this paper, we develop a machine learning model for detecting and classifying ransomware using classification techniques. The work trained supervised machine learning algorithms for building the model and used the test set to perform the model evaluation. The study used a confusion matrix to observe the proposed algorithm's model accuracy, which enabled a systematic comparison of each algorithm. Supervised algorithms used for this study are the naive Bayes, which resulted in an accuracy of 83.40% with the test set result, and Decision Tree with an accuracy of (J48) 97.60%, respectively. The models result indicated an increase in the accuracy of detection and classification of ransomware. � 2021 IEEE. en_US
dc.publisher Institute of Electrical and Electronics Engineers Inc. en_US
dc.subject Digital Files en_US
dc.subject digital kidnapping attacks en_US
dc.subject filtering en_US
dc.subject machine learning model en_US
dc.subject Ransomware en_US
dc.title Development of a Machine Learning Model for Detecting and Classifying Ransomware en_US
dc.type Conference Paper en_US


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