Deep Learning Approaches for Cyber Threat Detection and Mitigation

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dc.contributor.authorJuyal, A.
dc.contributor.authorBhushan, B.
dc.contributor.authorHameed, A.A.
dc.contributor.authorJamil, A.
dc.date.accessioned2024-05-19T14:33:46Z
dc.date.available2024-05-19T14:33:46Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description7th International Conference on Advances in Artificial Intelligence, ICAAI 2023 -- 13 October 2023 through 15 October 2023 -- -- 196685en_US
dc.description.abstractCyberspace has inflated over the past decade, primarily driven by pervasive development and widespread usage of the internet. Prolonged cyber-attacks and security vulnerabilities have become more common as a consequence. A recent study conducted by Ponemon Institute revealed that 64% of businesses have suffered web-based attacks in 2023. Thus, cybersecurity measures have become mandatory in every sector, that leverages technology, to protect confidential information from malignant threats. The majority of the sectors use conventional security measures like anti-virus products to defend their systems against outside attacks, however, these products fail to work on unseen and polymorphic threats. With cutting-edge deep learning (DL) processes getting adopted in nearly every field, it has been widely exploited in cybersecurity to detect and classify cyber-attacks. This study discusses the application of common deep learning models in cybersecurity to detect malware, intrusion, phishes, and spam. This study provides a description of deep learning models and their mathematical backgrounds that are common in cybersecurity. This study also reviews and analyses the work of other researchers who experimented with deep learning models in cybersecurity. This study delineates the challenges faced by deep learning models in cybersecurity. Finally, concluding remarks and future research directions are summarized to foresee the potential improvement of deep learning in cybersecurity. © 2023 ACM.en_US
dc.identifier.doi10.1145/3633598.3633605
dc.identifier.endpage41en_US
dc.identifier.isbn9798400708985
dc.identifier.scopus2-s2.0-85184132216en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage34en_US
dc.identifier.urihttps://doi.org/10.1145/3633598.3633605
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4331
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherAssociation for Computing Machineryen_US
dc.relation.ispartofACM International Conference Proceeding Seriesen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectCybersecurityen_US
dc.subjectDeep Learningen_US
dc.subjectIntrusion Detectionen_US
dc.subjectMachine Learningen_US
dc.subjectMalware Detectionen_US
dc.titleDeep Learning Approaches for Cyber Threat Detection and Mitigationen_US
dc.typeConference Objecten_US

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