Performance Analysis of NSL_KDD Data Set Using Neural Networks with Logistic Sigmoid Activation Unit
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.
KeywordsNetwork intrusion detection system NSL_KDD Neural networks Logistic sigmoid activation unit
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.
- 2.Aggarwal, P., Sharma, S.K.: Analysis of KDD dataset attributes—class wise for intrusion detection. In: 3rd International Conference on Recent Trends in Computing 2015 (ICRTC-2015)Google Scholar
- 4.Aziz, A.S.A., Azar, A.T., Hassanien, A.E, Hanafy, S.A.-O.: Continuous features discretization for anomaly intrusion detectors generation. In: Soft Computing in Industrial Applications, Volume 223 of the series Advances in Intelligent Systems and Computing, pp. 209–221Google Scholar
- 5.Hussain, J., Lalmuanawma, S.: Feature analysis, evaluation and comparisons of classification algorithms based on noisy intrusion dataset. In: 2nd International Conference on Intelligent, Computing, Communication & Convergence (ICCC-2016)Google Scholar
- 6.Singh, S., Bansal, M.: Improvement of intrusion detection system in data mining using neural network. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(9),1124–1130Google Scholar
- 7.Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.: A detailed analysis of the KDD CUP 99 data set. In: Second IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA) (2009, submitted)Google Scholar