Cutting Edge '25

QuanNetDetect – Quantum Hybrid Deep Learning Model Framework for Detecting Encrypted TLS Malicious Network Traffic

By

Catagories

Play Video

The research project is proposed as a novel approach for detecting encrypted malicious TLS network traffic by using Quantum Deep Learning. Without decrypting the traffic, by using the metadata (packet-level) information alone, the model can do the detection. The project involved multiclassification on TLS-based encrypted attacks and binary classification over TLS-based malicious network traffic by using Quantum Computing and Quantum Deep Learning.

Vision Quest

Check out the visionary projects our students have brought up in this year
VisuaLit

VisuaLit is an AI-powered eBook reader that redefines traditional reading by merging visual storytelling, audio narration, and contextual learning into…

VenDoor

The VenDoor application is a fully functional mobile application designed to create a bridge between mobile vendors and their customers…

UniGuide

UniGuide is a student-focused platform that helps individuals make smart educational and career decisions. It offers a comprehensive database of…