MUMBAI, India, Jan. 9 -- Intellectual Property India has published a patent application (202541112304 A) filed by New Prince Shri Bhavani College Of Engineering And Technology; Vignesh S; Venkatesan M; Santhosh K; and D. R. Anita Sofia Liz, Chennai, Tamil Nadu, on Nov. 17, 2025, for 'adaptive defense: zero day attack detection in nids with deep reinforcement learning.'
Inventor(s) include Vignesh S; Venkatesan M; Santhosh K; and D. R. Anita Sofia Liz.
The application for the patent was published on Jan. 9, under issue no. 02/2026.
According to the abstract released by the Intellectual Property India: "This project proposes an adaptive defense framework for Network Intrusion Detection Systems (NIDS) that detects zero-day attacks by combining Deep Reinforcement Learning (DRL), Recurrent Neural Networks (RNNs), and fuzzy logic. A lightweight traffic preprocessor derives flow and temporal features; stacked GRU/LSTM layers model sequence dynamics, feeding a DRL agent that learns policies for anomaly scoring and response. A fuzzy inference layer encodes expert rules and converts uncertain indicators (e.g., entropy, rare port use, burstiness) into interpretable risk grades, which regularize training and calibrate alerts. The reward balances detection rate, latency, and false positives, while curriculum and experience replay speed convergence under streaming concept drift. On benchmark traces and live traffic, the hybrid DRL-RNN-fuzzy NIDS adapts without signatures or frequent retraining, improves time-to-detect, and reduces alert fatigue compared with conventional supervised baselines, providing resilient protection for modem, high-throughput networks in practice."
Disclaimer: Curated by HT Syndication.
