Fog-Based Security Framework for Intelligent Traffic Light Control Systems

International Journal of Emerging Research in Science, Engineering, and Management
Vol. 2, Issue 3, pp. 358-365, March 2026.

https://doi.org/10.58482/ijersem.v2i3.46

This work is licensed under a Creative Commons Attribution 4.0 International License.

Fog-Based Security Framework for Intelligent Traffic Light Control Systems

R. S. Subhasreevignani

Arikatla Yamini

M. Reddy Prasad

D Tharun

Department of CSE, Siddharth Institute of Engineering & Technology, Puttur, AP, India.

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Abstract: Intelligent Traffic Light Control Systems (ITLCS) are foundational components of modern smart cities, designed to alleviate urban congestion, optimize traffic throughput, and bolster overall road safety. However, the increasing connectivity of these systems exposes them to critical cybersecurity vulnerabilities, including data tampering, signal spoofing, unauthorized network access, and Distributed Denial-of-Service (DDoS) attacks. Such malicious interventions can lead to catastrophic traffic disruption, delayed emergency services, and systemic infrastructure failure, necessitating a robust and resilient security architecture. This project proposes a Fog-based security framework to address these challenges by decentralizing intelligence at the network edge. By deploying fog computing nodes in close physical proximity to intersections, the system enables real-time data processing and low-latency decision-making, ensuring continuous operation even during cloud connectivity outages. The security posture of the ITLCS is significantly enhanced through the integration of lightweight cryptographic protocols, multi-factor authentication, and specialized Intrusion Detection Systems (IDS) tailored for resource-constrained environments. By shifting security verification from a distant cloud to localized fog nodes, this framework provides a high-reliability, proactive defense mechanism that ensures the integrity and availability of critical transportation infrastructure.

Keywords: Herbal plant recognition, neural architectures, convolutional networks, lightweight models, sequence learning.

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This article is part of Volume 2, Issue 3 (March 2026)