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Temporal Dynamics and Security Assessment of SRT Protocol: A 96-Day Longitudinal Analysis of Production Streaming Infrastructure

Ahmed Fouad Kadhim Koysha · 2026 · Journal of Communications

DOI: 10.12720/jcm.21.4.544-564 · cited by 0 works (OpenAlex) · open-access PDF

French

Cette étude met en évidence une analyse longitudinale de 96 jours du comportement du protocole Secure Reliable Transport (SRT) dans un environnement de streaming de production entre l'Irak et la Turquie, examinant 262 672 échantillons métriques de transport et 5 033 104 mesures de ressources système collectées entre octobre 2025 et janvier 2026. La recherche aborde les modèles temporaires, les relations système-ressource, la vulnérabilité d'attaque et la détection d'anomalies. Les résultats révèlent une variation temporelle significative: la reconnaissance négative présente une variation horaire de 7,6 fois et une variation quotidienne de 11,05 fois (p < 0,001), bien que avec de petites tailles d'effet (eta^2 < 0,04), la recherche indique que la période tempor

Machine translation (Meta M2M100); the English original follows.

Abstract

This study presents a 96-day longitudinal analysis of Secure Reliable Transport (SRT) protocol behavior in a production streaming environment between Iraq and Turkey, examining 262,672 transport metric samples and 5,033,104 system resource measurements collected between October 2025 and January 2026. The research addresses temporal patterns, system-resource relationships, attack vulnerability, and anomaly detection. Results reveal significant temporal variation: Negative Acknowledgment exhibits 7.6-fold hourly variation and 11.5-fold daily variation (p < 0.001), though with small effect sizes (eta^2 < 0.04), indicating that temporal period explains less than 4 percent of total variance. Central Processing Unit (CPU) utilization emerges as the strongest system-level correlate of transport variation (r = 0.617 with NAK rate). Controlled User Datagram Protocol (UDP) flood experiments at 500 packets per second cause CPU increases of 176 to 806 percent, while SRT transport metrics change by only plus or minus 13 percent demonstrating protocol resilience through the Automatic Repeat Request mechanism. An unexpected anomaly reaching 97.7 percent CPU enabled signature-based differentiation between attack types. A Z-score anomaly detection method outperforms fixed thresholds, detecting all 7 security events. This study contributes the longest temporal characterization of SRT metrics reported to date, the first system-SRT correlation analysis, and the first controlled security assessment of SRT infrastructure. These findings provide actionable guidance for capacity planning, security hardening, and multi-metric monitoring of production SRT deployments.

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Topics: Computer science, Dynamics (music), Production (economics), Real-time computing, System dynamics, Computer network

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