An Analysis For Advanced Anomaly Detection Techniques Tailored For HTTP- Based Iot Systems

The rapid proliferation of the Internet of Things (IoT) has introduced significant security and reliability challenges, particularly in HTTP-based communication, which serves as a backbone for IoT data exchange. This research addresses the critical need for advanced anomaly detection and removal techniques tailored to HTTP traffic in IoT systems. By leveraging machine learning algorithms, including deep learning models, this study develops a comprehensive framework to identify, classify, and mitigate HTTP anomalies such as malicious attacks, protocol deviations, and unexpected behaviors. A benchmark dataset of real-world IoT HTTP traffic is curated, and extensive experimentation demonstrates the superiority of the proposed techniques over existing methods, achieving 95.2% detection accuracy and a 3.1% false positive rate. The research contributes novel anomaly classification frameworks, protocol-level filtering strategies, and practical deployment guidelines, significantly enhancing IoT security and reliability. Ethical considerations, scalability, and societal implications are thoroughly discussed, positioning this work as a pivotal advancement in securing HTTP-based IoT ecosystems.