  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.
