IoT Network Traffic Security Management Analysis
Keywords:
IOT Traffic Management, Federated Learning, Intrusion Detection, Artificial Intelligence, Machine LearningAbstract
The swift expansion of Internet of Things (IoT) devices has resulted in an unparalleled surge in network traffic, hence presenting noteworthy obstacles for the administration of security. This study examines the approaches used today to analyse and secure Internet of Things (IoT) network traffic, emphasising the critical role that machine learning (ML) and artificial intelligence (AI) techniques play in this process. These technologies provide strong solutions for dynamic and expansive IoT environments by improving anomaly detection, predictive maintenance, and real-time threat response.
Though they have potential, there are still a few drawbacks. The intricacy and diversity of Internet of Things networks pose difficulties for the standardisation of security standards. Furthermore, the incorporation of AI/ML models requires large amounts of data for training, which might present privacy issues and require a significant amount of processing power. The implementation of AI/ML models is further complicated by their vulnerability to adversarial attacks.
Future research should concentrate on creating AI/ML algorithms that are adaptable and lightweight for IoT devices with limited resources. Standardised frameworks that guarantee security and interoperability across various IoT systems should also be prioritised. Important research issues include federated learning to address data privacy concerns and strengthening the resilience of AI/ML models against adversarial threats. The next generation of IoT network security solutions can be more secure, efficient, and effective by addressing these restrictions.
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