Multi-Modal and CNN-Based Approaches in Cybersecurity: A Comprehensive Review

The rapid evolution of cyber threats has necessitated increasingly sophisticated detection methodologies that transcend traditional signature-based approaches. In recent years, Convolutional Neural Networks (CNNs) and multi-modal deep learning architectures have emerged as powerful paradigms for addressing diverse cybersecurity challenges, ranging from malware detection and phishing identification to network intrusion detection and encrypted traffic classification. This paper presents a comprehensive review of 50 recent research works (20222025) that leverage CNN-based and multi-modal learning techniques in the cybersecurity domain. We systematically categorize these approaches into seven distinct groups: (1)~CNN-based malware visualization and detection, (2)~CNN-based phishing and web threat detection, (3)~multi-modal and hybrid intrusion detection systems, (4)~CNN-based IoT and botnet detection, (5)~encrypted and darknet traffic classification, (6)~adversarial and GAN-based cybersecurity methods, and (7)~multi-modal cybersecurity applications. For each category, we analyze core methodologies, architectural innovations, and performance benchmarks. Furthermore, we provide detailed comparisons of datasets, evaluation metrics, and identify critical gaps in current research. Our analysis reveals that while CNN-based visualization achieves remarkable accuracy ($>$98\%) in controlled environments, significant challenges remain in adversarial robustness, cross-domain generalization, real-time deployment, and the effective fusion of heterogeneous data modalities. We conclude by outlining open challenges and future research directions that can advance the field toward more resilient, adaptive, and deployable cybersecurity systems.