ML_WNIDS

Since the beginning of the pandemic, the internet traffic has been considerably increased. Daily tasks such as working meetings or classes lectures were moved to remote modal- ity, and this transition involved the adoption of digital plat- form for different kind of transaction, in which, for most of the cases, personal/sensitive information is being requesting and/or sharing. Wireless network connection is turning in the most popular internet access connectivity protocol, and as the traffic also increased the number of security flaws are being revealed by intruders, making this protocol vulnera- ble. That is one of the reasons why it is important to im- plement systems capable to automate the detection of vul- nerabilities accurately in a network. Therefore, in this work, we propose a Machine Learning (ML) approach for the de- velopment of a Intrusion Detection System(IDS) for wireless networks that have be both characteristics: accurate and ex- plainable. To achieve those characteristics, we implemented a variety of feature selection methods, including feature im- portance, chi-square analysis, variance correlation analysis and explainable artificial intelligence. Our ML based IDS achieves an accuracy of 99.81% for multi-class classification with a reduced set of features. An description of the feature selection output is provided for better explanation of the pre- dicted outputs.