A Generic Framework for Ransomware Prediction and Classification with Artificial Neural Networks

Saaman Nadeem, Tahir Mehmood, Muhammad Yaqoob, Yap Bee Wah (Editor), Dhiya Al-Jumeily OBE (Editor), Michael W. Berry (Editor)

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With the increase in the development of technology, the threat of “Ransomware” has also increased especially towards organizations. Ransomware is a malicious software that encrypts all the user’s data or system and demands a ransom payment for decryption. Despite various machine learning approaches proposed for ransomware detection, they often fail to identify those threats accurately in time, thus leading to data loss and victimization. This research introduces a novel framework, primarily based on static analysis of ransomware and predicting the presence of ransomware on users’ systems by monitoring a defined set of ransomware activities. In this study, we used the Resilient Information Systems Security (RISS) ransomware dataset, encompassing 582 ransomware samples from 11 distinct families and 982 instances of goodware. We proposed a generic neural network framework for the identification of ransomware and compared the performance of artificial neural networks (ANN) and deep neural networks (DNN) in terms of accurately classifying ransomware and goodware. The suggested framework secured an accuracy of 98.56% with ANNs, and achieved a slightly better performance (99.06%) when ANN was replaced with DNN. Our results showed that a basic ANN can achieve performance comparable to that of a DNN for ransomware detection. In future work, we plan to evaluate the performance of the proposed framework in a real-time setting.
Original languageEnglish
Title of host publicationData Science and Emerging Technologies
Subtitle of host publicationProceedings of DaSET 2023
EditorsYap Bee Wah, Dhiya Al-Jumeily OBE, Michael W. Berry
PublisherSpringer Nature
Pages137-148
Number of pages12
ISBN (Electronic)978-981-97-0293-0, 978-981-97-0293-0
ISBN (Print)978-981-97-0292-3
DOIs
Publication statusE-pub ahead of print - 27 Apr 2024
EventThe International Conference on Data Science and Emerging Technologies DaSET 2023 - Virtual conference at UNITAR International University, Malaysia
Duration: 4 Dec 20235 Dec 2023
Conference number: 2
https://icdaset.com/daset2023/

Publication series

NameLecture Notes on Data Engineering and Communications Technologies
PublisherSpringer
Volume191
ISSN (Print)2367-4512
ISSN (Electronic)2367-4520

Conference

ConferenceThe International Conference on Data Science and Emerging Technologies DaSET 2023
Abbreviated titleDaSET 2023
Country/TerritoryMalaysia
Period4/12/235/12/23
Internet address

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