MUMBAI, India, July 24 -- Intellectual Property India has published a patent application (202621062276 A) filed by Muskan Sihare; Srikant Singh; Nimesh R. Patel; Akansha Saini; Ms. Jahnvi Balvantbhai Masrani; Dr. Jaynesh Desai; and Dr. Rubini. P on May 16, 2026, for Ai-Based Adaptive Intrusion Detection System For Real-Time Network Threat Prevention.

Inventors include Muskan Sihare; Srikant Singh; Nimesh R. Patel; Akansha Saini; Ms. Jahnvi Balvantbhai Masrani; Dr. Jaynesh Desai; and Dr. Rubini. P.

The application for the patent was published on July 17, 2026, under issue no. 29/2026.

Abstract: AI-BASED ADAPTIVE INTRUSION DETECTION SYSTEM FOR REAL-TIME NETWORK THREAT PREVENTION ABSTRACT The present invention relates to an AI-based adaptive intrusion detection system designed for real-time network threat prevention. The system continuously monitors network traffic, user behavior, device activity, and data flow patterns to identify suspicious or abnormal activities with high accuracy. Unlike conventional rule-based security systems, the proposed system uses machine learning and deep learning models that dynamically learn from evolving cyberattack patterns, including malware, denial-of- service attacks, phishing attempts, unauthorized access, and insider threats. The system includes a traffic data collection module, feature extraction unit, adaptive threat analysis engine, risk scoring module, and automated response controller. Upon detecting a potential intrusion, the system generates instant alerts and initiates preventive actions such as blocking malicious traffic, isolating affected nodes, updating firewall rules, or notifying administrators. The adaptive learning mechanism improves detection accuracy over time by analyzing new attack signatures and behavioral deviations. This invention enhances network security by reducing false positives, minimizing response time, and providing proactive protection against known and unknown cyber threats in enterprise, cloud, and IoT environments.

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