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An investigation of scalable anomaly detection techniques for a large network of Wi-Fi hotspots
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| dc.contributor.author |
Machaka, P
|
|
| dc.contributor.author |
Bagula, A
|
|
| dc.date.accessioned | 2015-08-19T11:15:04Z | |
| dc.date.available | 2015-08-19T11:15:04Z | |
| dc.date.issued | 2015 | |
| dc.identifier.citation | 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 | en_US |
| dc.identifier.isbn | 978-3319-168678 | |
| dc.identifier.uri | http://link.springer.com/chapter/10.1007%2F978-3-319-16868-5_7 | |
| dc.identifier.uri | http://hdl.handle.net/10204/8101 | |
| dc.description | 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 | en_US |
| dc.description.abstract | 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. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Springer | en_US |
| dc.relation.ispartofseries | Workflow;14817 | |
| dc.subject | Performance Monitoring | en_US |
| dc.subject | Neural Networks | en_US |
| dc.subject | Artificial Immune Systems | en_US |
| dc.subject | Bayesian Networks | en_US |
| dc.subject | Anomaly Performance Detection | en_US |
| dc.subject | Multilayer Perceptron | en_US |
| dc.subject | Naive Bayes | en_US |
| dc.subject | AIRS2 | en_US |
| dc.title | An investigation of scalable anomaly detection techniques for a large network of Wi-Fi hotspots | en_US |
| dc.type | Conference Presentation | en_US |
| dc.identifier.apacitation | 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 | en_ZA |
| dc.identifier.chicagocitation | 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 | en_ZA |
| dc.identifier.vancouvercitation | 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 . | en_ZA |
| dc.identifier.ris | TY - Conference Presentation AU - Machaka, P AU - Bagula, A AB - 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. DA - 2015 DB - ResearchSpace DP - CSIR KW - Performance Monitoring KW - Neural Networks KW - Artificial Immune Systems KW - Bayesian Networks KW - Anomaly Performance Detection KW - Multilayer Perceptron KW - Naive Bayes KW - AIRS2 LK - https://researchspace.csir.co.za PY - 2015 SM - 978-3319-168678 T1 - An investigation of scalable anomaly detection techniques for a large network of Wi-Fi hotspots TI - An investigation of scalable anomaly detection techniques for a large network of Wi-Fi hotspots UR - http://hdl.handle.net/10204/8101 ER - | en_ZA |
