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.
MUD Maker source code
IoTServer
Using mqtt implemented a simple publish and subscribe model
A Software Framework for Context-Sensitive Pervasive Computing
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.
[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.
Contiki implementation in C of the Ad-hoc On-demand Distance Vector (AODV) routing protocol, plus GUI.
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