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Adaptive Defense Zero-Day Attack Detection in NIDS with Deep Reinforcement Learning

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₹5,500.00

Aim:

             Design and deliver a lightweight, adaptive, and high-generalization intrusion detection framework that accurately identifies zero-day and known cyberattacks in network traffic while maintaining efficient real-time performance.

 
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BGL-PhishNet: Phishing Website Detection Using Hybrid Model-BERT, GNN, and LightGBM

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₹5,500.00
Aim: This study aims to develop an efficient and scalable system for multi-class classification of URLs into Phishing, Benign, Defacement, and Malware categories using the lightweight and context-aware DistilBERT model.
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DroneGuard: An Explainable and Efficient Machine Learning Framework for Intrusion Detection in Drone Networks

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₹5,500.00

Aim:

          Design and deliver a lightweight, interpretable, and efficient intrusion detection framework that detects GPS-spoofing and Denial-of-Service (DoS) attacks in drone networks in (near) real time while producing human-readable explanations for each alarm.

 
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PermGuard: A Scalable Framework for Android Malware Detection Using Permission-to-Exploitation Mapping

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₹5,500.00

Aim:

            To develop a robust and efficient system for detecting Android malware by advanced machine learning, and deep learning models trained on the CICMalDroid2020 dataset.

 
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