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dc.contributor.authorOmopintemi, A.H.
dc.contributor.authorGhafir, Ibrahim
dc.contributor.authorEltanani, S.
dc.contributor.authorKabir, Sohag
dc.contributor.authorLefoane, Moemedi
dc.date.accessioned2023-12-19T17:00:44Z
dc.date.accessioned2024-02-02T15:58:10Z
dc.date.available2023-12-19T17:00:44Z
dc.date.available2024-02-02T15:58:10Z
dc.date.issued2023-12
dc.identifier.citationOmopintemi AH, Ghafir I, Eltanani S et al (2023) Machine Learning for Malware Detection in Network Traffic. The 7th International Conference on Future Networks & Distributed Systems. Dec 21-22, Dubai, United Arab Emirates.en_US
dc.identifier.urihttp://hdl.handle.net/10454/19792
dc.descriptionNoen_US
dc.description.abstractDeveloping advanced and efficient malware detection systems is becoming significant in light of the growing threat landscape in cybersecurity. This work aims to tackle the enduring problem of identifying malware and protecting digital assets from cyber-attacks. Conventional methods frequently prove ineffective in adjusting to the ever-evolving field of harmful activity. As such, novel approaches that improve precision while simultaneously taking into account the ever-changing landscape of modern cybersecurity problems are needed. To address this problem this research focuses on the detection of malware in network traffic. This work proposes a machine-learning-based approach for malware detection, with particular attention to the Random Forest (RF), Support Vector Machine (SVM), and Adaboost algorithms. In this paper, the model’s performance was evaluated using an assessment matrix. Included the Accuracy (AC) for overall performance, Precision (PC) for positive predicted values, Recall Score (RS) for genuine positives, and the F1 Score (SC) for a balanced viewpoint. A performance comparison has been performed and the results reveal that the built model utilizing Adaboost has the best performance. The TPR for the three classifiers performs over 97% and the FPR performs < 4% for each of the classifiers. The created model in this paper has the potential to help organizations or experts anticipate and handle malware. The proposed model can be used to make forecasts and provide management solutions in the network’s everyday operational activities.en_US
dc.language.isoenen_US
dc.subjectMachine learningen_US
dc.subjectMalware detectionen_US
dc.subjectIntrusion detectionen_US
dc.subjectMalware analysisen_US
dc.subjectAdaboost algorithmen_US
dc.subjectRandom foresten_US
dc.subjectK-nearest neighbor algorithmen_US
dc.titleMachine Learning for Malware Detection in Network Trafficen_US
dc.status.refereedYesen_US
dc.date.Accepted2023-12-09
dc.date.Accepted2023-12-09
dc.typeConference paperen_US
dc.type.versionNo full-text in the repositoryen_US
dc.rights.licenseUnspecifieden_US
dc.date.updated2023-12-19T17:00:45Z
refterms.dateFOA2024-02-02T15:58:39Z
dc.relation.urlhttps://icfnds.org/
dc.openaccess.statusclosedAccessen_US


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