Intellectual Property India Publishes Patent Application for Intelligent Hybrid Deep Learning Framework For Dark Web Profiling And Cyber Threat Prediction.

MUMBAI, India, June 22 -- Intellectual Property India has published a patent application (202641070677 A) filed by Koneru Lakshmaiah Education Foundation; and Vinod Babu Bollikonda on June 06, 2026, for Intelligent Hybrid Deep Learning Framework For Dark Web Profiling And Cyber Threat Prediction..

Inventors include Koneru Lakshmaiah Education Foundation; and Vinod Babu Bollikonda.

The application for the patent was published on June 12, 2026, under issue no. 24/2026.

Abstract: The present invention relates to an intelligent hybrid deep learning framework for dark web profiling and cyber threat prediction within anonymous communication environments. The framework comprises a dark web crawling module, a preprocessing and adaptive feature conditioning engine, a hierarchical classification engine, a graph-based webpage profiling mechanism, a fingerprint extraction module, an adversarial prediction engine, and an adaptive optimization controller. The system continuously acquires and analyzes hidden webpage contents, communication metadata, hyperlink structures, browsing behavior, network characteristics, and associated cyber intelligence information from dark web environments. A hybrid computational architecture combining Bayesian neural reasoning, hierarchical attention mechanisms, Siamese neural comparison networks, and ResNeXt-based feature extraction enables accurate classification of illicit dark web activities and prediction of adversarial cyber behavior. The framework further generates behavioral fingerprints based on browser characteristics, operating system parameters, cache behavior, cookies, application layer interactions, and communication patterns to establish graph-based intelligence relationships. An explainability mechanism provides confidence-based intelligence outputs, feature importance rankings, and behavioral evidence traces for forensic analysis and cyber investigation. A feedback-driven optimization mechanism continuously updates model parameters, classification thresholds, and behavioral reasoning logic based on evolving dark web activities. The invention provides a scalable, adaptive, and technically effective cyber intelligence framework for cybercrime investigation, law enforcement, digital forensics, national security, and cyber threat monitoring applications.

Disclaimer: Curated by HT Syndication.