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Intrusion Detection System with SVM and Ensemble Learning Algorithms

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Abstract

One of the most effective methods of training a model for intrusion detection requires a very good selection of features from the data and efficient and robust training algorithms to facilitate a better prediction model. Choosing features scoring above a certain threshold allows for the removal of unrelated features following which makes the job of a prediction model easier. The study aims to identify and select the highly correlated features after feature reduction for training the model and then employ various machine learning algorithms to make the classifications with tree-based ensemble learning techniques and non-linear SVM. The dataset from NSL-KDD which is a version derived from the KDD’99 Cup dataset is considered. Implementation is carried out in Python 3 using the scikit-learn machine learning library which is built upon SciPy. Further, the performances of various machine learning classifiers will be evaluated to test for and compare the detection metrics.

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Data availability

The dataset used and analyzed during the current study are available from the corresponding author on reasonable request.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Manipur, Imphal, India

    Khundrakpam Johnson Singh, Debabrata Maisnam & Usham Sanjota Chanu

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  1. Khundrakpam Johnson Singh
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  2. Debabrata Maisnam
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  3. Usham Sanjota Chanu
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Correspondence to Khundrakpam Johnson Singh.

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This article is part of the topical collection “Research Trends in Computational Intelligence” guest edited by Anshul Verma, Pradeepika Verma, Vivek Kumar Singh and S. Karthikeyan.

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Johnson Singh, K., Maisnam, D. & Chanu, U.S. Intrusion Detection System with SVM and Ensemble Learning Algorithms. SN COMPUT. SCI. 4, 517 (2023). https://doi.org/10.1007/s42979-023-01954-3

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