Aditya-1500/Bot-IoT
The project aims to analyse different types of attacks using the Bot-IoT dataset and also apply & compare different classification algorithms. In the project, machine learning algorithms are applied and tested using ten best features from the dataset.
This helps network security analysts and IoT system administrators evaluate the effectiveness of different machine learning models for detecting cyberattacks in IoT networks. It takes network traffic data from IoT environments, specifically the Bot-IoT dataset, and outputs insights into which classification algorithms best identify various attack types. This is for professionals focused on safeguarding IoT infrastructure from malicious activities.
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Use this if you need to understand how well different machine learning models can detect botnet and other attacks within an IoT network environment using a realistic dataset.
Not ideal if you are looking for a ready-to-deploy intrusion detection system or if your primary focus is on preventing attacks rather than analyzing them.
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Oct 19, 2021
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