A comprehensive toolkit for applying Machine Learning and Data-Driven approaches to digital forensics and cyber security investigations. Features network traffic analysis, memory forensics integration with Volatility 3, and CASE-compliant data handling.

Major project for Advanced Topics in Computer Science. Using mitmproxy to automatically detect if private data has been leaked in network traffic data by certain android applications.

Modeling Network Traffic using Recurrent Neural Networks (Long Short-Term Memory, LSTM)

Deployment guide for CloudLens network visibility software on Google Cloud Platform

ML pipelines for cybersecurity: packet flow feature engineering, automated LightGBM tuning, HTTP malware detection

Generating neural networks for diverse networking classification tasks via hardware-aware neural architecture search, Transactions on Computers 2023

A machine learning project to detect cyberattacks in IoT healthcare networks. Utilizes PCA for dimensionality reduction, data visualization for insights, and ANN for classification. Features a FastAPI backend and Streamlit UI for inference with labeled and unlabeled datasets.

Real-time network packet capture and analysis using Moloch (Arkime), Wireshark, and Elastic Stack to detect anomalies, visualize patterns, and enhance cybersecurity.

NexoOps is an Intelligent Network Management System which summarizes log files, classify alerts and uses a chatbot to show real time network traffic through commands

A machine learning implementation for network traffic classification, which can be used for network intrusion detection and traffic pattern analysis.

Basic Network Traffic Analysis using K-Means and PCA algorithms.

Course assignments of COL334:- Computer Networks course at IIT Delhi under Professor Tarun Mangla

A network sniffer application that captures and analyzes network traffic using machine learning to detect malicious activity. Integrated with Kafka for real-time event streaming and Flask for a web interface that provides real-time alerts. Fully Dockerized for easy deployment.

Advanced network traffic forecasting framework using SARIMA time series models on CESNET-TimeSeries-2023-2024 dataset. Includes automated retraining, comprehensive evaluation metrics (RMSE, SMAPE, R²), and production-ready HPC batch processing scripts.

https://gitlab.com/mini-ids/network_traffic_analysis

This project demonstrates the identification and documentation of malicious network activity using packet capture (PCAP) files and Wireshark.

In this course, learn cybersecurity analysis using Wireshark and Tshark. Master packet capture, filtering, protocol analysis, and automation for effective network security monitoring.