Intellectual Property India Publishes Patent Application for Anomnet: An Ai-Based Real-Time Network Intrusion Detection And Threat Classification System Using Convolutional Neural Networks

MUMBAI, India, July 7 -- Intellectual Property India has published a patent application (202641081059 A) filed by Malla Reddy Engineering College For Women Autonomous; Malla Reddy University; Malla Reddy Mr Deemed To Be University; and Malla Reddy Vishwavidyapeeth Deemed on July 01, 2026, for Anomnet: An Ai-Based Real-Time Network Intrusion Detection And Threat Classification System Using Convolutional Neural Networks.

Inventors include Dr. Y. Madhaveelatha; Dr. David Livingston; Mr. Munimanda Prem Chander; Mr. Kumaraswamy Kankala; Ms. Geetha Prathibha Kotla; Dr. Priyanka Dash; Mr. Jangpalli Srinivas; and Dr. Syeda Husna Mehanoor.

The application for the patent was published on July 03, 2026, under issue no. 27/2026.

Abstract: With the rapid expansion of digital communication and internet-based services, network security has become a critical concern for organizations and individuals. Traditional intrusion detection systems mainly rely on signature-based methods that detect only known threats and often fail to identify new or evolving cyberattacks. The proposed invention, AnomNet, introduces an intelligent real-time network intrusion detection and threat classification system using Artificial Intelligence and Deep Learning techniques. The system utilizes a Convolutional Neural Network (CNN) to analyze network traffic patterns and detect anomalies in real time. AnomNet captures live network packets, extracts relevant traffic features, preprocesses the data, and classifies the traffic into normal or malicious categories. The system further identifies specific attack types such as Denial of Service (DoS), Probe attacks, Remote to Local (R2L), and User to Root (U2R). The proposed platform integrates packet capture mechanisms, deep learning-based classification, and a graphical user interface to provide real-time monitoring and alert notifications. By automating threat detection and classification, AnomNet enhances cybersecurity efficiency, reduces manual monitoring effort, and improves the ability to detect unknown attacks. This invention contributes to the development of AI-driven intelligent cybersecurity systems capable of strengthening digital infrastructure protection.

Disclaimer: Curated by HT Syndication.