Voting Classification Method for Network Traffic Prediction
Altaf Hussain Shah1, M. Mazhar Afzal2
1Altaf Hussain Shah*, Department of Computer Engineering Glocal University Saharanpur, UP.
2Dr M. Mazhar Afzal, Associate Professor & HOD of computer engineering Glocal University Saharanpur, UP.
Manuscript received on April 20, 2020. | Revised Manuscript received on April 25, 2020. | Manuscript published on May 10, 2020. | PP: 510-515 | Volume-9 Issue-7, May 2020. | Retrieval Number: E3104039520/2020©BEIESP | DOI: 10.35940/ijitee.E3104.059720
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The prediction analysis is the approach of data mining which is applied to predict future possibilities based on the current information. The network traffic classification is the major issue of the prediction analysis due to complex dataset. The network traffic techniques have three steps, which are pre processing, feature extraction and classification. In the phase of pre-processing data set is collected which is processed to removed missing and redundant values. In the second phase, the relationship between attribute and target set is established. In the last phase, the technique of classification is applied for the classification. This research study has been influenced by the different intrusion threats on internet and the ways to detect them. In this research, we have studied and analyzed the famous network traffic data -NSL KDD dataset and its various features. The proposed model is a hybrid of Logistic Regression and K nearest neighbor classifier combined using voting classifier, which aims at classifying the data into malicious and non malicious with more accuracy than existing methods.
Keywords: Network traffic analysis, feature extraction, classification, UCI repository.
Scope of the Article: Classification