CLDM-MMNNs: Cross-layer defense mechanisms through multi-modal neural networks fusion for end-to-end cybersecurity—Issues, challenges, and future directions

Abstract

Cybersecurity threats have grown in complexity and scale, necessitating robust defense mechanisms that integrate multiple layers of network security. Multi-modal neural networks (MMNNs) have emerged as a powerful tool for addressing such challenges due to their ability to process and integrate heterogeneous data sources. This review provides an in-depth analysis of cross-layer defense mechanisms that leverage MMNNs for end-to-end cybersecurity. The study explores the foundational principles of MMNNs, their applications in intrusion detection, malware analysis, anomaly detection, and advanced persistent threat (APT) mitigation. The paper emphasizes the synergy between multi-modal data integration and neural network architectures, enabling real-time threat detection and adaptive response. By categorizing existing approaches and highlighting key advancements, this review outlines current limitations, including computational overhead and model interpretability, and suggests future research directions for developing efficient, scalable, and explainable MMNN-based defense systems.