unsw-nb15-dataset

A Python framework for N-agent collective learning using Tsetlin Machines and synthetic data sharing, supporting zero-shot sensor onboarding and LLM-driven feature extraction.

Deep Learning with Hybrid CNN-LSTM Architecture

Real-time Intrusion Detection System using Signature-Based Detection, DNS Threat Intelligence, and Machine Learning Attack Classification.

ML-powered network anomaly detection system for identifying anomalous network traffic using Flask and Scikit-learn.

Federated Learning IDS — Privacy Attack Analysis

Machine Learning based Network Traffic Forensics research project using the UNSW-NB15 dataset for cyber crime investigation and intrusion detection. This B.Tech CSE (AI & ML) final semester research paper 2022 - 2026 focuses on supervised Machine Learning models including Decision Tree, Random Forest, and Linear SVM .

This project utilizes Apache Hadoop, Hive, and PySpark to process and analyze the UNSW-NB15 dataset, enabling advanced query analysis, machine learning modeling, and visualization. The project demonstrates efficient data ingestion, processing, and predictive analytics for network security insights.

Machine learning analysis of the UNSW-NB15 cybersecurity dataset using R with logistic regression and random forest models.

A practical coursework-style project from my Master's studies in Big Data Analytics (at University of East London), showcasing hands-on use of big data tools and techniques on a real-world cyber-security dataset.

End-to-end ML intrusion detection system on UNSW-NB15 with Random Forest, FastAPI, Docker and monitoring

Détection de cyberattaques par Temporal Graph Network (TGN) sur graphe dynamique de communication IP — PyTorch Geometric, jeu de données UNSW-NB15

Network traffic classification using Machine Learning

SOC-ready cybersecurity system for network intrusion detection using hybrid machine learning (Isolation Forest + Random Forest) with SMOTE-based imbalance handling and SOC-style security analytics on UNSW-NB15.