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Siber Güvenlikte Çok Modlu Makine Öğrenmesi

Year 2025, Volume: 1 Issue: 1, 47 - 55, 31.05.2025

Pelin Yıldırım Taşer , Mustafa Murat Taşer

Abstract

Çok Modlu Makine Öğrenmesi (MML), metin, resim, ses vb. gibi farklı biçimlerdeki bilgileri entegre ederek daha zengin ve anlamlı temsiller elde etmeyi amaçlayan, gelişmiş bir makine öğrenmesi yaklaşımıdır. MML modelleri, günlük dosyaları, sistem etkinliği verileri, ağ trafiği verileri ve daha fazlası dahil olmak üzere çeşitli kaynaklardan gelen verileri entegre ederek siber tehditlerin tespiti ve azaltılmasının doğruluğunu ve sağlamlığını artırabilir. Bu motivasyonu göz önünde bulundurarak, bu makale alanda yayınlanmış araştırmaların kapsamlı bir listesini inceleyerek, siber güvenlikte MML'nin mevcut durumuna genel bir bakış sunmaktadır. Çalışmanın kapsamı, MML'nin üç yeni siber güvenlik kategorisine (tehdit tespiti, sosyal mühendislik saldırıları ve veri gizliliği ve şifreleme) katkılarının analizini içerir. Ardından, MML modellerinin geleneksel yaklaşımların etkisiz olduğu çeşitli sorunları çözmek için siber güvenlik alanında nasıl uygulanabileceği tartışıldı. Ayrıca, bu çalışmada 2010 ile 2024 yılları arasında siber güvenlik alanında makine öğrenimi (ML), derin öğrenme (DL) ve MML yaklaşımlarının artan eğilimi sunulmaktadır. Ayrıca bu incelemede MML’nin siber güvenlik açısından avantajları ve zorlukları da açıkça ortaya konmaktadır.

Keywords

Derin Öğrenme Makine öğrenmesi Çok Modlu Makine Öğrenmesi Siber Güvenlik

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Multimodal Machine Learning in Cybersecurity

Year 2025, Volume: 1 Issue: 1, 47 - 55, 31.05.2025

Pelin Yıldırım Taşer , Mustafa Murat Taşer

Abstract

Multimodal Machine Learning (MML) is an improved machine learning approach that aims to obtain richer and more meaningful representations by integrating information from different modalities, such as text, image, audio, and so on. MML models can enhance the accuracy and robustness of the detection and mitigation of cyber threats by integrating data from several sources, including log files, system activity data, network traffic data, and more. Considering this motivation, this paper gives an overview of the present state of MML in cybersecurity by examining a thorough list of published research in the field. The study's scope includes the analysis of MML's contributions to three emerging cyber security categories: threat detection, social engineering attacks, and data privacy, and encryption. Afterward, how MML models can be applied in the cybersecurity field to solve various issues where conventional approaches are ineffective was discussed. Furthermore, the increasing trend of machine learning (ML), deep learning (DL), and MML approaches within the cybersecurity field between the years 2010 and 2024 are presented in this study. Besides, this review also clearly states the advantages and challenges of MML with regard to cybersecurity.

Keywords

Deep Learning Machine Learning Multimodal Machine Learning Cybersecurity

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There are 58 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence (Other)
Journal Section Reviews
Authors

Pelin Yıldırım Taşer 0000-0002-5767-2700

Mustafa Murat Taşer

Early Pub Date May 30, 2025
Publication Date May 31, 2025
Submission Date April 17, 2025
Acceptance Date May 8, 2025
Published in Issue Year 2025 Volume: 1 Issue: 1

Cite

IEEE P. Yıldırım Taşer and M. M. Taşer, “Multimodal Machine Learning in Cybersecurity”, INNAI, vol. 1, no. 1, pp. 47–55, 2025.
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