MUMBAI, India, Aug. 10 -- Intellectual Property India has published a patent application (202641090982 A) filed by Mohan Babu University on July 27, 2026, for Fusion-Based Wi-Fi Intrusion Detection System For Multi-Channel Man-In-The-Middle Attack Prevention.

Inventors include Mr. P. Yogendra Prasad; Ms. P. Uzma Anjum; Ms. K. Kavya; Mr. R. Ravi Teja; and Ms. Y. Sri Pujitha.

The application for the patent was published on July 31, 2026, under issue no. 31/2026.

Abstract: The present invention relates to a fusion-based Wi-Fi intrusion detection system for detecting Multi-Channel Man-in-the-Middle (MC-MitM) attacks using adaptive anomaly detection and machine learning techniques. The system comprises a network traffic acquisition module, preprocessing module, feature extraction module, multiple machine learning classifiers, a fusion decision module, and an intrusion reporting module. Wireless communication data is continuously captured, processed, and transformed into feature vectors representing network behavior. The extracted features are analyzed using Random Forest, Support Vector Machine, and Isolation Forest algorithms, each independently generating intrusion predictions. A fusion mechanism combines the outputs of multiple classifiers to produce a unified detection result with improved accuracy and reduced false alarms. Upon identification of malicious activity, the system generates real-time security alerts and records intrusion events for further analysis and forensic investigation. The modular architecture supports integration of additional detection algorithms and future cybersecurity technologies while maintaining compatibility with existing wireless infrastructures. The disclosed invention provides a scalable, adaptive, and cost-effective solution capable of detecting both known and unknown Multi-Channel Man-in-the-Middle attacks in enterprise networks, smart homes, industrial systems, educational institutions, and IoT environments, thereby significantly enhancing wireless communication security and intrusion detection performance.

Disclaimer: Curated by HT Syndication.