Data Analysis of Wireless Networks Using Classification Techniques

TL;DR

This paper evaluates various classification techniques for analyzing wireless network data to detect anomalies and improve intrusion detection, using WEKA software to assess their effectiveness.

Contribution

It provides an analysis of classification algorithms applied to wireless network data for anomaly detection, highlighting their success rates and potential for intrusion detection systems.

Findings

Classification algorithms show high success rates in data classification.

Effective detection of normal and abnormal network traffic is achievable.

Potential application in wireless network intrusion detection systems.

Abstract

In the last decade, there has been a great technological advance in the infrastructure of mobile technologies. The increase in the use of wireless local area networks and the use of satellite services are also noticed. The high utilization rate of mobile devices for various purposes makes clear the need to track wireless networks to ensure the integrity and confidentiality of the information transmitted. Therefore, it is necessary to quickly and efficiently identify the normal and abnormal traffic of such networks, so that administrators can take action. This work aims to analyze classification techniques in relation to data from Wireless Networks, using some classes of anomalies pre-established according to some defined criteria of the MAC layer. For data analysis, WEKA Data Mining software (Waikato Environment for Knowledge Analysis) is used. The classification algorithms present a success rate in the classification of viable data, being indicated in the use of intrusion detection systems for wireless networks.