The paper seeks to investigate the use of scalable machine learning techniques to address anomaly detection problem in a large Wi-Fi network. This was in the efforts of achieving a highly scalable preemptive monitoring tool for wireless networks. The Neural Networks, Bayesian Networks and Artificial Immune Systems were used for this experiment. Using a set of data extracted from a live network of Wi-Fi hotspots managed by an ISP; we integrated algorithms into a data collection system to detect anomalous performance over several test case scenarios. The results are revealed and discussed in terms of both anomaly performance and statistical significance.
Reference:
Machaka, P and Bagula, A. 2015. An investigation of scalable anomaly detection techniques for a large network of Wi-Fi hotspots. In: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Scalable Information Systems, vol. 139, pp 71-79
Machaka, P., & Bagula, A. (2015). An investigation of scalable anomaly detection techniques for a large network of Wi-Fi hotspots. Springer. http://hdl.handle.net/10204/8101
Machaka, P, and A Bagula. "An investigation of scalable anomaly detection techniques for a large network of Wi-Fi hotspots." (2015): http://hdl.handle.net/10204/8101
Machaka P, Bagula A, An investigation of scalable anomaly detection techniques for a large network of Wi-Fi hotspots; Springer; 2015. http://hdl.handle.net/10204/8101 .
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Vol. 139, pp 71-79. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website
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