A computational framework for IoT security integrating deep learning-based semantic algorithms for real-time threat response

The growth of IoT networks has led to significant security issues, especially in areas of real-time threat detection and response. This research paper presents a hybrid deep learning and semantic reasoning framework that enhances threat intelligence and autonomous response. The proposed research framework integrates Convolutional Neural Networks for spatial anomaly detection and Recurrent Neural Networks for sequential pattern recognition. Concurrently, a semantic contextualization layer utilizes knowledge graphs for context-aware threat detection. The model is highly computational and energy efficient, incorporating path-breaking Edge Computing and Real-Time Stream Processing paradigms, facilitating low-latency identification of highly dynamic advanced attacks like APTs and DDoS. During this research study, extensive statistical validation was performed using the CICIoT 2023 dataset and a custom Internet of Things testbed, demonstrating high accuracy, scalability, and adaptability across diverse IoT environments. The paper also outlines privacy, ethical considerations, and regulatory compliance (GDPR, CCPA) to ensure responsible deployment. This research contributes to next-generation autonomous IoT security solutions, bridging deep learning, semantic reasoning, and real-world security challenges, with future work focusing on real-world deployments and adaptive threat intelligence.