Abstract
This paper aims to classify network traffic in order to segregate normal and anomalous traffic. There can be multiple classes of network attacks, so a multiclass model is implemented for ordering attacks in anomalous traffic. A supervised machine learning method SVM support Vector Machine has been used for multiclass classification. The most widely used dataset KDD Cup 99 has been used for analysis. Firstly, the dataset has been preprocessed using three way step and secondly the analysis has been performed using multi-classifier method. The results acquired exhibited the adequacy of the multiclass classification on the dataset to a fair extent.
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Kaur, P., Chaudhary, P., Bijalwan, A., Awasthi, A. (2018). Network Traffic Classification Using Multiclass Classifier. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2018. Communications in Computer and Information Science, vol 905. Springer, Singapore. https://doi.org/10.1007/978-981-13-1810-8_21
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DOI: https://doi.org/10.1007/978-981-13-1810-8_21
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