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Anomaly Detection in Network Traffic Using Advanced Machine Learning Techniques | Python IEEE Final Year Project 2025 - 2026.
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🔗Email: [email protected],
🌐Website: https://www.jpinfotech.org

📌Our Proposed Project Title: Machine Learning based Intrusion Detection System for Detecting Various Attacks
💡Implementation: Python.
🔬Algorithm / Model Used: CatBoost Classifier, ExtraTree Classifier, Gradient Boosting Classifier.
🌐Web Framework: Flask.
🖥️Frontend: HTML, CSS, JavaScript.
💰Cost (In Indian Rupees): Rs.5000/

📘Project Abstract:
👉The exponential growth of internet usage has heightened the need for robust cybersecurity mechanisms to detect and prevent malicious activities in network traffic.
👉This project, titled "Anomaly Detection in Network Traffic Using Advanced Machine Learning Techniques," focuses on developing a Intrusion Detection System (IDS) that leverages powerful machine learning models to identify and classify various types of cyber-attacks. Built using Python as the core programming language, with HTML, CSS, and JavaScript for the front-end and Flask as the web framework, the system provides an interactive platform for real-time anomaly detection.

🚀IEEE Base Paper Title: Anomaly Detection in Network Traffic Using Advanced Machine Learning Techniques.

📍REFERENCE:
STEPHANIE NESS, VISHWANATH ESWARAKRISHNAN, HARISH SRIDHARAN, VARUN SHINDE, NAGA VENKATA PRASAD JANAPAREDDY, AND VINEET DHANAWAT, “Anomaly Detection in Network Traffic Using Advanced Machine Learning Techniques”, IEEE ACCESS, VOLUME 13, 2025.

🏷️tags:
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