Abstract
Ransomware is a continuing threat and 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 behaviour. In this paper, we demonstrate a classification technique of integrating both static and dynamic features to increase the accuracy of detection and classification of ransomware. We train supervised machine learning algorithms using a test set and use a confusion matrix to observe accuracy, enabling a systematic comparison of each algorithm. In this work, supervised algorithms such as the Naïve Bayes algorithm resulted in an accuracy of 96% with the test set result, SVM 99.5%, random forest 99.5%, and 96%. We also use Youden's index to determine sensitivity and specificity.
Original language | English |
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Title of host publication | Intelligent Data Engineering and Automated Learning - IDEAL 2019 |
Subtitle of host publication | 20th International Conference, Manchester, UK, November 14-16, 2019, Proceedings, Part II |
Editors | Hujun Yin, David Camacho, Peter Tino, Antonio J. Tallón-Ballesteros, Ronaldo Menezes, Richard Allmendinger |
Place of Publication | Cham |
Publisher | Springer International Publishing |
Pages | 45-52 |
Number of pages | 8 |
Volume | LNCS11872 |
ISBN (Electronic) | 9783030336172 |
ISBN (Print) | 9783030336165, 3030336166 |
DOIs | |
Publication status | Published - 24 Oct 2019 |
Event | 20th International Conference on Intelligent Data Engineering and Automated Learning - University of Manchester, Manchester, United Kingdom Duration: 14 Nov 2019 → 16 Nov 2019 Conference number: 20 http://www.datascience.manchester.ac.uk/events-1/events/20th-international-conference-on-intelligent-data-engineering-and-automated-learning-ideal/ |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer |
Name | |
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Volume | LNCS 11872 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 20th International Conference on Intelligent Data Engineering and Automated Learning |
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Abbreviated title | IDEAL |
Country/Territory | United Kingdom |
City | Manchester |
Period | 14/11/19 → 16/11/19 |
Internet address |