Next Article in Journal
LoS Theoretical and Experimental MIMO Study from 1–40 GHz in Indoor Environments
Previous Article in Journal
Asynchronous Floating-Point Adders and Communication Protocols: A Survey
Open AccessArticle

A Machine Learning Based Two-Stage Wi-Fi Network Intrusion Detection System

Electrical and Computer Engineering Department, College of Engineering and Sciences, Purdue University Northwest, Hammond, IN 46323, USA
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in Francisco D Vaca and Quamar Niyaz, “An Ensemble Learning Based Wi-Fi Network Intrusion Detection System (WNIDS).” In Proceedings of the 17th IEEE International Symposium on Network Computing and Applications (NCA 2018), Cambridge, MA, USA, 1–3 November 2018.
Electronics 2020, 9(10), 1689; https://doi.org/10.3390/electronics9101689
Received: 16 September 2020 / Revised: 11 October 2020 / Accepted: 11 October 2020 / Published: 15 October 2020
(This article belongs to the Special Issue Artificial Intelligence for Cybersecurity)
The growth of wireless networks has been remarkable in the last few years. One of the main reasons for this growth is the massive use of portable and stand-alone devices with wireless network connectivity. These devices have become essential on the daily basis in consumer electronics. As the dependency on wireless networks has increased, the attacks against them over time have increased as well. To detect these attacks, a network intrusion detection system (NIDS) with high accuracy and low detection time is needed. In this work, we propose a machine learning (ML) based wireless network intrusion detection system (WNIDS) for Wi-Fi networks to efficiently detect attacks against them. The proposed WNIDS consists of two stages that work together in a sequence. An ML model is developed for each stage to classify the network records into normal or one of the specific attack classes. We train and validate the ML model for WNIDS using the publicly available Aegean Wi-Fi Intrusion Dataset (AWID). Several feature selection techniques have been considered to identify the best features set for the WNIDS. Our two-stage WNIDS achieves an accuracy of 99.42% for multi-class classification with a reduced set of features. A module for eXplainable Artificial Intelligence (XAI) is implemented as well to understand the influence of features on each type of network traffic records. View Full-Text
Keywords: Wi-Fi networks; network intrusion detection system (NIDS); machine learning (ML); explainable artificial intelligence (XAI) Wi-Fi networks; network intrusion detection system (NIDS); machine learning (ML); explainable artificial intelligence (XAI)
Show Figures

Figure 1

MDPI and ACS Style

A. Reyes, A.; D. Vaca, F.; Castro Aguayo, G.A.; Niyaz, Q.; Devabhaktuni, V. A Machine Learning Based Two-Stage Wi-Fi Network Intrusion Detection System. Electronics 2020, 9, 1689.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop