Intrusion Detection in Wi-Fi Networks by Modular and Optimized Ensemble of Classifiers

Giuseppe Granato, Alessio Martino, Luca Baldini, Antonello Rizzi

2020

Abstract

With the breakthrough of pervasive advanced networking infrastructures and paradigms such as 5G and IoT, cybersecurity became an active and crucial field in the last years. Furthermore, machine learning techniques are gaining more and more attention as prospective tools for mining of (possibly malicious) packet traces and automatic synthesis of network intrusion detection systems. In this work, we propose a modular ensemble of classifiers for spotting malicious attacks on Wi-Fi networks. Each classifier in the ensemble is tailored to characterize a given attack class and is individually optimized by means of a genetic algorithm wrapper with the dual goal of hyper-parameters tuning and retaining only relevant features for a specific attack class. Our approach also considers a novel false alarm management procedure thanks to a proper reliability measure formulation. The proposed system has been tested on the well-known AWID dataset, showing performances comparable with other state of the art works both in terms of accuracy and knowledge discovery capabilities. Our system is also characterized by a modular design of the classification model, allowing to include new possible attack classes in an efficient way.

Paper Citation

in Harvard Style

Granato G., Martino A., Baldini L. and Rizzi A. (2020). Intrusion Detection in Wi-Fi Networks by Modular and Optimized Ensemble of Classifiers. In Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - Volume 1: NCTA; ISBN 978-989-758-475-6, SciTePress, pages 412-422. DOI: 10.5220/0010109604120422

in Bibtex Style

@conference{ncta20,
author={Giuseppe Granato and Alessio Martino and Luca Baldini and Antonello Rizzi},
title={Intrusion Detection in Wi-Fi Networks by Modular and Optimized Ensemble of Classifiers},
booktitle={Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - Volume 1: NCTA},
year={2020},
pages={412-422},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010109604120422},
isbn={978-989-758-475-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - Volume 1: NCTA
TI - Intrusion Detection in Wi-Fi Networks by Modular and Optimized Ensemble of Classifiers
SN - 978-989-758-475-6
AU - Granato G.
AU - Martino A.
AU - Baldini L.
AU - Rizzi A.
PY - 2020
SP - 412
EP - 422
DO - 10.5220/0010109604120422
PB - SciTePress