Rademics Research Institute

Abstract

Spatiotemporal deep learning models have emerged as transformative tools for monitoring and mitigating cyber threats in surveillance data streams. These models effectively capture complex spatial and temporal dependencies, enabling real-time detection and prediction of malicious activities. This chapter explores advanced architectures, including hybrid models, transformer-based frameworks, and graph neural networks, optimized for cybersecurity applications. Key challenges such as adversarial robustness, scalability, and resource efficiency on edge devices are critically examined. Innovative solutions are proposed, including ensemble techniques for enhanced reliability and capsule networks tailored for spatiotemporal data. The chapter provides a comprehensive evaluation of performance metrics and presents methodologies for integrating these advanced models into real-world cyber monitoring systems. This work contributes to the growing body of research on deploying deep learning for secure, adaptive, and efficient cyber threat management in dynamic data environments.

Introduction

Cybersecurity has become a critical priority across industries as the digital landscape expands, with increasingly sophisticated threats targeting vulnerable systems [1]. Surveillance data streams, characterized by high volumes of continuously evolving information, present unique challenges for threat detection [2]. Spatiotemporal deep learning models have emerged as essential tools to address these complexities [3]. Unlike traditional approaches, these models capture both spatial and temporal dependencies in data, enabling more accurate and timely threat identification [4,5]. Their capacity to process dynamic patterns in real-time makes them invaluable for monitoring diverse cybersecurity domains, including anomaly detection in networks and the prevention of malware propagation [6-8].

Recent advances in deep learning have significantly enhanced the capabilities of spatiotemporal models [9]. Architectures such as hybrid models combining convolutional neural networks (CNNs) and recurrent neural networks (RNNs), transformers, and graph neural networks (GNNs) have proven effective in capturing intricate data relationships [10,11]. These advanced models not only improve predictive accuracy but also enable scalability across large-scale surveillance systems [12-14]. The integration of innovative techniques such as attention mechanisms and ensemble learning ensures robustness and adaptability, addressing the evolving nature of cyber threats [15].