This repository contains the code and implementation for experiments detailed in the research paper on AI for IoT Attack Classification. The aim of this project is to leverage advanced machine learning techniques to classify and detect cyberattacks in IoT environments with high accuracy.
Features
- Implements state-of-the-art machine learning algorithms for IoT attack classification.
- Handles preprocessing, feature extraction, and dataset management.
- Supports a wide range of attacks simulated on IoT systems.
- Provides metrics for model evaluation, including accuracy, precision, recall, and F1-score.
If you use this code in your research, please cite:
@inproceedings{BUT189196,
author="Viet Anh {Phan} and Jan {Jeřábek} and Lukáš {Malina}",
title="Comparison of Multiple Feature Selection Techniques for Machine Learning-Based Detection of IoT Attacks",
booktitle="ARES '24: Proceedings of the 19th International Conference on Availability, Reliability and Security",
year="2024",
pages="1--10",
publisher="Association for Computing Machinery",
address="New York, NY, USA",
doi="10.1145/3664476.3670440",
isbn="979-8-4007-1718-5",
url="https://dl.acm.org/doi/10.1145/3664476.3670440"
}