MUMBAI, India, Jan. 2 -- Intellectual Property India has published a patent application (202541124356 A) filed by Shaik Hasane Ahammad, Vijayawada, Andhra Pradesh, on Dec. 9, 2025, for 'advanced deep learning model for real-time iot intrusion and anomaly detection.'
Inventor(s) include M. Ravinder; V Sudha Rani; and Dr. Sk. Hasane Ahammad.
The application for the patent was published on Jan. 2, under issue no. 01/2026.
According to the abstract released by the Intellectual Property India: "The invention discloses an advanced deep learning-based system for real-time intrusion and anomaly detection in Internet of Things (IoT) environments. The system continuously collects and preprocesses heterogeneous network traffic, sensor data, and device metadata using an adaptive normalization and noise-filtering pipeline. A hybrid deep learning architecture combining Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and Attention mechanisms performs hierarchical spatial-temporal feature extraction to identify malicious, suspicious, and abnormal behaviors with high accuracy. The model incorporates online learning, drift detection, and auto-tuning thresholds to adapt dynamically to evolving IoT traffic patterns and zero-day threats. A cloud-edge hybrid deployment enables fast local predictions and high-complexity analysis in the cloud. The system further integrates explainable AI components, including attention heatmaps, anomaly timelines, and feature-importance visualizations, enabling transparent and interpretable threat decisions. Real-time alerts, severity scoring, and recommended mitigation actions provide immediate response capabilities. The invention offers a scalable, adaptive, and explainable security framework suitable for large, heterogeneous IoT networks, improving detection accuracy, reducing false positives, and strengthening real-time cyber defense."
Disclaimer: Curated by HT Syndication.
