Abstract
Ineffective and huge network data traffic hampers the efficiency of the intrusion detection system (IDS). Removing the irrelevant features from the high-dimensional dataset is very challenging due to its intrinsic dimension. So, we have implemented two feature reduction techniques, i.e., principal component analysis (PCA), linear discriminant analysis (LDA). Feature reduction techniques are also called dimensionality reduction techniques where dimensions of input features are reduced. Dimensionality reduction techniques increase the accuracy and speed of machine learning classifiers. We have compared the performance of PCA and LDA concerning accuracy, training time, and testing time by using Naïve Bayes and support vector machine (SVM) classifiers. LDA outperforms PCA for binary, multi-class classification of intrusion detection system for both the classifiers individually. SVM provides better classification accuracy than Naïve Bayes for PCA and LDA both.
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Chakraborty, A., Joardar, S. (2022). Intrusion Detection System Performance Comparison Using Dimensionality Reduction Techniques. In: Dahal, K., Giri, D., Neogy, S., Dutta, S., Kumar, S. (eds) Internet of Things and Its Applications. Lecture Notes in Electrical Engineering, vol 825. Springer, Singapore. https://doi.org/10.1007/978-981-16-7637-6_35
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DOI: https://doi.org/10.1007/978-981-16-7637-6_35
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