Rank-Based Univariate Selection for Intrusion Detection System
Winda Ayu Safitri, Tohari Ahmad · 2021
Intrusion Detection System (IDS) is a scheme, which supervises network traffic and monitors suspicious activities in a network system. Nowadays, a potential solution to efficiently detect network intrusions is to use a machine learning (ML)-based IDS system. There are numerous issues with IDS, mainly in the dataset for the training. One of the problems that often arises is increasing detection accuracy and minimizing computation time in training the data. There is a suitable dataset for detecting various intrusions, which is the NSL-KDD. In this dataset, there is a number of features that are redundant and irrelevant to access. We suggest a strategy in this study to increase IDS performance by combining univariate selection and Support Vector Machine (SVM) for classification. It is ideal for categorization in IDS because it has high performance. Data reduction is used to increase the accuracy and decrease computation time. The result of experiments shows that the proposed method effectively improves the accuracy.