Abstract
The growing amount of data has necessitated the use of Intrusion Detection Systems in the modern days. The performance of an IDS is determined by the feature selection and classifiers. The traditional IDS fail to give satisfactory performance in today’s world of growing data. We, in this paper, have propose a hybrid, two step approach for intrusion detection. In the first step, the data is classified into different classes with the help of Support Vector Machines. After the first steps, the records whose classification is not certain are passed on to the second step in which we use k-Nearest Neighbor method to further classify the incoming request into its respective class. Then we show how our proposed model compares with some recent approaches. We observe the proposed model to evaluate better on parameters like accuracy and precision.
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Singh, A., Singh, M., Kumar, K. (2021). A Hybrid Method for Intrusion Detection Using SVM and k-NN. In: Tripathi, M., Upadhyaya, S. (eds) Conference Proceedings of ICDLAIR2019. ICDLAIR 2019. Lecture Notes in Networks and Systems, vol 175. Springer, Cham. https://doi.org/10.1007/978-3-030-67187-7_13
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DOI: https://doi.org/10.1007/978-3-030-67187-7_13
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