(untitled) (Research, Society and Development, 2023)
The security present in IEEE 802.11 networks becomes more relevant every day. However, security on the IEEE 802.11 network has not kept pace with threats with as much significance. For this reason, the proposal arises to design an Intrusion Detection System-IDS based on machine learning that will be able to have self-improvement, since it will create a safe environment, capable of detecting all disguised threats, Deauthentication, EAPOL-Logoff and Beacon Flood, where they were launched on a real corporate network. With this, correlated the performance metrics, and among them, which values the quality of the classification, the Matthews Correlation Coefficient. The Deauthentication anomaly above the Naive Bayes classifier was obtained (88,71%), whereas the quality value of the Logistic Regression (Logistic) classifier was equated to (88,69%), and nevertheless, the J48 presented a lower value of (88,47%). Despite this, the identification of the Beacon Flood attack was due to the Naive Bayes algorithm showing the highest detection rate (100,00%), followed by Logistic (99,95%) and J48 having the lowest value (98,85 %). As a result, in the detection of the EAPOL-Logoff anomaly, the classifications presented similarity of (100,00%) and the others, with the presentation of a detection, due to non-anomalous data (Normal), the Naive Bayes was affected by (89,92%), followed by Logistic maintaining (89,89%), while J48 was tested with a lower rate (89,67%). With the study evidences provide the possibility that it is possible to develop an intrusion detection system based on wireless networks.