MUMBAI, India, June 26 -- Intellectual Property India has published a patent application (202621052033 A) filed by Moresh Madhukar Mukhedkar; Govind Awad; Khushi Parate; Anurag Kumar; Shweta Koparde; and Vivek Patil on April 23, 2026, for Containerised Ai-Driven Network Intrusion Detection System For Edge Computing Environments.
Inventors include Moresh Madhukar Mukhedkar; Govind Awad; Khushi Parate; Anurag Kumar; Shweta Koparde; and Vivek Patil.
The application for the patent was published on June 19, 2026, under issue no. 25/2026.
Abstract: The present invention relates to a containerised AI-driven Network Intrusion Detection System (NIDS) designed for edge computing environments to provide efficient and real-time detection of cyber threats. With the increasing adoption of edge computing and Internet of Things (IoT) devices, traditional centralized intrusion detection systems face challenges such as high latency, bandwidth consumption, and limited scalability. The proposed system integrates artificial intelligence (AI) and machine learning (ML) techniques with containerization technologies to enable lightweight, portable, and scalable deployment of intrusion detection components at the network edge. The system captures and preprocesses network traffic data, extracts relevant features, and analyzes them using trained AI models to detect anomalies and malicious activities, including previously unseen attacks. Each functional module, including data collection, preprocessing, detection, and alert generation, is deployed as an independent container, allowing efficient resource utilization and easy management across heterogeneous edge devices. The system further supports distributed detection, where multiple edge nodes collaborate and share threat intelligence to enhance detection accuracy and responsiveness. By performing analysis locally at the edge, the invention reduces dependency on centralized cloud infrastructure, minimizes detection latency, and improves overall network security. The proposed solution provides a scalable, adaptive, and efficient intrusion detection framework, suitable for modern distributed and resource-constrained computing environments.
Disclaimer: Curated by HT Syndication.
