MUMBAI, India, Feb. 6 -- Intellectual Property India has published a patent application (202641007371 A) filed by Dr. Rekha Gangula, Warangal, Telangana, on Jan. 25, for 'a deep neural network-based intrusion detection system by using machine learning interfaces for a cloud environment.'
Inventor(s) include Minumula Charitha; Dr Rekha Gangula; Cherala Saikiran; Pujari Shivakumar; Mrs. R. Swathi; Gosikonda Neeraja; Pothula Shireesha; Mohammad Arif Pasha; Nasam Rachana Devi; and J Lavanya.
The application for the patent was published on Feb. 6, under issue no. 06/2026.
According to the abstract released by the Intellectual Property India: "This invention presents a sophisticated deep neural network (DNN)-based intrusion detection system (IDS) optimized for cloud environments through advanced machine learning (ML) interfaces. As cloud adoption surges, vulnerabilities to cyber intrusions escalate, necessitating intelligent, scalable defenses. The system ingests diverse data streams-network traffic, system logs, and API interactions-via seamless ML interfaces compatible with platforms like AWS and Azure. A hybrid DNN architecture combines convolutional neural networks (CNNs) for spatial feature extraction, recurrent neural networks (RNNs) like LSTMs for temporal patterns, and autoencoders for anomaly baseline establishment. Federated learning ensures privacy-compliant training across edge devices, avoiding data centralization risks. In practice, the system achieves superior performance: in a case study with 10,000 simulated attacks, it detects 96% of threats with 1.5% false positives, outperforming traditional IDS by 20%. Explainable AI components, such as attention mechanisms, provide interpretable insights into detections, aiding compliance and forensics. Novel elements include reinforcement learning-driven adaptive thresholds and real-time auto-scaling for cloud elasticity. Applicable in sectors like finance for fraud prevention, healthcare for data security, and e-commerce for uptime assurance, this IDS minimizes breach impacts, potentially reducing annual losses by millions. Deployed as containerized services, it integrates effortlessly into existing infrastructures, promoting proactive cybersecurity. Overall, it advances cloud security by merging DNN prowess with ML interfaces for resilient, intelligent threat mitigation."
Disclaimer: Curated by HT Syndication.
