(untitled) (Eureka Journal of Education & Learning Technologies, 2026)
Modern cybersecurity systems face a growing challenge: simultaneously monitoring multiple threats, anomalous behavioral patterns, and malicious network entities across large-scale, dynamic environments. This paper introduces a novel framework Cyber MOT that adapts the methodological foundations of Online Multi-Object Tracking (MOT) to the domain of cybersecurity threat detection and network intrusion monitoring. By establishing a rigorous structural analogy between visual object tracking and the persistent tracking of cyber threat actors across sequential network telemetry, we demonstrate that state-of-the-art deep learning architectures including transformer-based association models and dual-stream appearance-motion encoders can be effectively repurposed for tracking lateral movement, advanced persistent threats (APTs), and coordinated attack campaigns within enterprise-scale networks. Experimental evaluations conducted on both standard MOT benchmarks and simulated network intrusion datasets confirm that CyberMOT achieves superior tracking accuracy, substantially reduced identity-switch errors, and improved real-time processing efficiency compared to conventional intrusion detection baselines. The proposed framework introduces an identity-consistency loss function designed to explicitly penalize tracking failures attributable to adversarial identity obfuscation tactics,such as IP rotation and MAC address spoofing. Results indicate that deep learning-driven MOT paradigms represent a promising and underexplored frontier for advancing next-generation cybersecurity architectures, warranting further investigation.