Intrusion Detection Model Based on TF.IDF and C4.5 Algorithms

Authors : Khaldoon AWADH, Ayhan AKBAŞ

Pages : 1691-1698

Doi:10.2339/politeknik.693221

View : 50 | Download : 17

Publication Date : 2021-12-01

Article Type : Research Paper

Abstract :In recent years, the use of machine learning and data mining technologies has drawn researchers’ attention to new ways to improve the performance of Intrusion Detection Systems insert ignore into journalissuearticles values(IDS);. These techniques have proven to be an effective method in distinguishing malicious network packets. One of the most challenging problems that researchers are faced with is the transformation of data into a form that can be handled effectively by Machine Learning Algorithms insert ignore into journalissuearticles values(MLA);. In this paper, we present an IDS model based on the decision tree C4.5 algorithm with transforming simulated UNSW-NB15 dataset as a pre-processing operation. Our model uses Term Frequency.Inverse Document Frequency insert ignore into journalissuearticles values(TF.IDF); to convert data types to an acceptable and efficient form for machine learning to achieve high detection performance. The model has been tested with randomly selected 250000 records of the UNSW-NB15 dataset. Selected records have been grouped into various segment sizes, like 50, 500, 1000, and 5000 items. Each segment has been, further, grouped into two subsets of multi and binary class datasets. The performance of the Decision Tree C4.5 algorithm with Multilayer Perceptron insert ignore into journalissuearticles values(MLP); and Naive Bayes insert ignore into journalissuearticles values(NB); has been compared in Weka software. Our proposed method significantly has improved the accuracy of classifiers and decreased incorrectly detected instances. The increase in accuracy reflects the efficiency of transforming the dataset with TF.IDF of various segment sizes.
Keywords : IDS, TF IDF, Data Mining, Network Security, machine learning

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