Impact Factor (2025): 6.9
DOI Prefix: 10.47001/IRJIET
This
research project takes a holistic strategy to improve QoS and security in
SDN-based IoT networks. We offer an intricate solution with a federated
autoencoder-based tool to precisely identify malicious network traffic in
response to the expanding IoT ecosystem and its security issues. We use a QoS
dashboard coupled with OpenDaylight to monitor latency, throughput, packet
loss, and jitter, dynamic resource allocation based on device priorities, and
better MQTT AES encryption. These elements constitute a framework for SDN-based
IoT networks' changing needs. Global autoencoder models with client models on
IoT devices provide real-time anomaly detection in our system. We offer a
packet manipulator to generate tailored network traffic samples for robust model
training. The OpenDaylight-integrated QoS dashboard gives administrators
real-time network performance data. A priority-based resource allocation system
allocates resources by device significance to preserve QoS during peak demand.
AES encryption for MQTT, a popular IoT standard, is also improved. It protects
critical IoT data from security risks. Our Mininet-based Ubuntu simulations
show that our technique maintains QoS, detects network anomalies, and
strengthens IoT network security. These findings show that our methodology may
improve SDN-based IoT deployment reliability and security, creating a more resilient
and secure ecosystem.
Country : Sri Lanka
IRJIET, Volume 7, Issue 11, November 2023 pp. 170-176