MAC Address Classification in Privacy Issue Using Gaussian Naïve Bayes

Abstract

There have been several initiatives within standards committees to overcome privacy issues, including user tracking activity based on Media Access Control (MAC) addresses. The implementation of randomized MAC addresses on captive portals, with user-specific connection limits to address privacy concerns, introduces some problems. To address this issue, device removal based on OUI classification was proposed. Connection data taken from the RADIUS server were divided into two distinct classes, either random or not. Gaussian Naïve Bayes was utilized to classify the data with 16 distinct thresholds, and the solution with the highest accuracy was selected. The research produced results showing that all classifications had an accuracy above 96%. Values of 6 and 50% for Mac address thresholds and random percentage thresholds gave the highest accuracy of 98.1139%. This indicates that random Mac address classification in the real world can be done using the result.

Keywords

• captive portal, gaussian naïve bayes, mac, privacy, randomization