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

Topics: Applications: Image Processing and Artificial Vision, Pattern Recognition, Decision Making, Industrial and Real World Applications, Financial Applications, Neural Prostheses and Medical Applications, Neural Based Data Mining and Complex Information Process; Learning Paradigms and Algorithms; Stochastic Learning and Statistical Algorithms

Authors: Giuseppe Granato ; Alessio Martino ; Luca Baldini and Antonello Rizzi

Affiliation: Department of Information Engineering, Electronics and Telecommunications, University of Rome “La Sapienza”, via Eudossiana 18, 00184 Rome, Italy

ISBN: 978-989-758-475-6

Keyword(s): Information Granulation, Data Clustering, Supervised Learning, Genetic Algorithms, Malicious Traffic Detection, Network Intrusion Detection Systems.

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 th e 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. (More)

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Paper citation in several formats:
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 - Volume 1: NCTA, ISBN 978-989-758-475-6, pages 412-422. DOI: 10.5220/0010109604120422

@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 - Volume 1: NCTA,},
year={2020},
pages={412-422},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010109604120422},
isbn={978-989-758-475-6},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computational Intelligence - 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

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