Applying Natural Language Processing for detecting malicious patterns in Android applications

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dc.authoridAlam, Shahid/0000-0002-4080-8042
dc.contributor.authorAlam, Shahid
dc.date.accessioned2025-01-06T17:43:46Z
dc.date.available2025-01-06T17:43:46Z
dc.date.issued2021
dc.description.abstractWith increasing quantity and sophistication, malicious code is becoming difficult to discover and analyze. Modern NLP (Natural Language Processing) techniques have significantly improved, and are being used in practice to accomplish various tasks. Recently, many research works have applied NLP for finding ma-licious patterns in Android and Windows apps. In this paper, we exploit this fact and apply NLP tech-niques to an intermediate representation (MAIL e Malware analysis intermediate language) of Android apps to build a similarity index model, named SIMP. We use SIMP to find malicious patterns in Android apps. MAIL provides control flow patterns to enhance the malware analysis and makes the code accessible to NLP techniques for checking semantic similarities. For applying NLP, we consider a MAIL program as one document. The control flow patterns in this program when divided, into specific blocks (words), become sentences. We apply TFIDF and Bag-of-Words over these control flow patterns to build SIMP. Our proposed model, when tested with real malware and benign Android apps using different validation methods, achieved an MCC (Mathews Correlation Coefficient) > 0.94 between the true and predicted values. That indicates, predicting a new sample either as malware or benign with a high success rate. (c) 2021 Elsevier Ltd. All rights reserved.
dc.identifier.doi10.1016/j.fsidi.2021.301270
dc.identifier.issn2666-2817
dc.identifier.scopus2-s2.0-85122659800
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.fsidi.2021.301270
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2790
dc.identifier.volume39
dc.identifier.wosWOS:000709481500004
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofForensic Science International-Digital Investigation
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectNatural language processing
dc.subjectAndroid applications
dc.subjectControl flow patterns
dc.subjectIntermediate language
dc.subjectMalicious patterns
dc.titleApplying Natural Language Processing for detecting malicious patterns in Android applications
dc.typeArticle

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