iot-networks

MUD Maker source code

An advanced deep learning solution for detecting IoT botnet attacks, specifically Mirai, leveraging the power of GNNs. This project transforms raw IoT device network traffic into dynamic graph structures and employs a GraphSAGE model for robust and efficient anomaly detection, enhancing the security posture of interconnected devices.

IoTServer

IoT/sensor Test and Simulation platform.

Using mqtt implemented a simple publish and subscribe model

A Software Framework for Context-Sensitive Pervasive Computing

[Anomaly detection] refers to the process of identifying patterns in data that do not conform to expected behavior. This project aims to develop a machine learning model to predict and identify potential attacks in IoT networks, thus helping to secure these networks from malicious activities.

This project focuses on implementing an advanced IoT network custom protocol using the RPL (Routing Protocol for Lossy networks) with a time-window expiration parameter. The primary goals are to handle power consumption and reduce network congestion.

AI/ML-based DDoS Detection System for IoT & IIoT Networks using CICIDS2023 dataset, Random Forest model for real-time anomaly detection and traffic classification.

Contiki implementation in C of the Ad-hoc On-demand Distance Vector (AODV) routing protocol, plus GUI.