Avoiding Future Digital Extortion through Robust Protection against Ransomware Threats Using Deep Learning Based Adaptive Approaches

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dc.contributor.authorSharmeen S.
dc.contributor.authorAhmed Y.A.
dc.contributor.authorHuda S.
dc.contributor.authorKocer B.S.
dc.contributor.authorHassan M.M.
dc.date.accessioned2020-03-26T20:20:42Z
dc.date.available2020-03-26T20:20:42Z
dc.date.issued2020
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractDigital extortion has become a major cyber risk for many organizations; small-medium enterprises (SME) to large enterprises business and individual entrepreneurs. Ransomware is a kind of malware that is the main threat to digital extortion and has caused many organizations to lose huge revenue by paying much bigger ransom demands to the cybercriminals in recent years. The explosive growth of ransomware is due to the existing large infection vector such as social engineering, email attachment, zip file download, browsing malicious site, infected search engine which are boosted dramatically by easily available cryptographic tools, Ransomware As a Service (RaaS), increased cloud storage and off-the-self ransomware toolkits. The large infection vector and available toolkits not only grew ransomware extremely, but also made them more obfuscated, encrypted and varying patterns in the new variants. This, in turn, caused the conventional supervised analysis and detection engine to fail to detect the new variants of ransomware. This paper addresses the limitations of conventional supervised detection engine and proposes semi-supervised framework to compute the inherent latent sources of the varying patterns in the new variants in an unsupervised way using deep learning approaches. The proposed framework extracts the inherent characteristics in the varying patterns from the unlabelled ransomware obtained from the wild which is scalable to accommodate upcoming malicious executables. Then the unsupervised learned model is combined with supervised classification, thus constructing an adaptive detection model. The proposed framework has been verified using real ransomware data with a dynamic analysis testbed. Our extensive experimental results and discussion demonstrate that the proposed adaptive framework can successfully identify different variants of ransomware and achieve higher performance than existing supervised approaches. © 2013 IEEE.en_US
dc.description.sponsorshipKing Saud University: RSP-2019/18en_US
dc.description.sponsorshipThe authors are grateful to King Saud University, Riyadh, Saudi Arabia, for funding this work through Researchers Supporting Project number RSP-2019/18.en_US
dc.identifier.doi10.1109/ACCESS.2020.2970466en_US
dc.identifier.endpage24534en_US
dc.identifier.issn2169-3536en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage24522en_US
dc.identifier.urihttps://dx.doi.org/10.1109/ACCESS.2020.2970466
dc.identifier.urihttps://hdl.handle.net/20.500.12395/38649
dc.identifier.volume8en_US
dc.identifier.wosWOS:000524652500009en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofIEEE Accessen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectadaptive approachesen_US
dc.subjectdeep learningen_US
dc.subjectDigital extortionen_US
dc.subjectransomwareen_US
dc.titleAvoiding Future Digital Extortion through Robust Protection against Ransomware Threats Using Deep Learning Based Adaptive Approachesen_US
dc.typeArticleen_US

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