Abstract
Network intrusion detection system (NIDS) is a software tool that scans network traffic and performs security analysis on it. NIDS performs match operations upon passing traffic with a pre-established library of attacks in order to identify attacks or abnormal behavior. One of the standard data sets used widely for network intrusion systems is the NSL_KDD data set. The current paper aims to analyze the NSL_KDD data set using artificial neural network with sigmoid activation unit in order to perform a metric analysis study that is aimed at discovering the best fitting parameter values for optimal performance of the given data. Evaluation measures such as accuracy, F-measure, detection rate, and false alarm rate will be used to evaluate the efficiency of the developed model.
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Acknowledgements
This work is supported by the Science and Engineering Research Board (SERB), Ministry of Science & Technology, Govt. of India under Grant No. SB/FTP/ETA-0180/2014.
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Jannela, V., Rodda, S., Uppuluru, S.N.R., Koratala, S.C., Chandra Mouli, G.V.S.S.S. (2018). Performance Analysis of NSL_KDD Data Set Using Neural Networks with Logistic Sigmoid Activation Unit. In: Satapathy, S., Bhateja, V., Das, S. (eds) Smart Computing and Informatics . Smart Innovation, Systems and Technologies, vol 77. Springer, Singapore. https://doi.org/10.1007/978-981-10-5544-7_18
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DOI: https://doi.org/10.1007/978-981-10-5544-7_18
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