Nur Iman, Alif (2020) Metode Reduksi Data dengan Deteksi Outlier untuk Optimasi Fitur Seleksi dalam Model Intrusion Detection System. Masters thesis, Institute of Technology Sepuluh Nopember.
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Abstract
With the development and ease of access to internet networks, the potential for attacks and intrusions has also increased. The intrusion detection system (IDS) is an approach to overcome this problem. IDS are divided into two models; signature- based and anomaly-based. Modeling an anomaly-based IDS can be done by machine learning; one of the schemes in machine learning is data reduction. IDS datasets are usually obtained through a real-time process that has undefined proportional data. The purpose of data reduction is to speed up the process and optimize the data to improve the accuracy, precision, and specifications. There are several methods to perform data reduction, one of which uses outlier detection techniques. A proper outlier detection will be influential in improving the classification results of machine learning. In this study, the outlier is formed by a circle which generated from the k- means clustering of all features selected. Two scenarios will be evaluated; a circle generated from two points of the minimum and maximum cluster and median of all clusters. A comparison of proposed methods using feature selection from previous studies has been carried out with evaluation metrics. Our empirical results show that the proposed method can improve the performance of feature selection and classification in the intrusion detection system modeling.
| Item Type: | Thesis (Masters) |
|---|---|
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) > TA158.7 Computer network resources |
| Divisions: | Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Informatics Engineering > 55101-(S2) Master Thesis |
| Depositing User: | Alif Nur Iman |
| Date Deposited: | 01 Sep 2020 03:26 |
| Last Modified: | 03 Jun 2023 15:07 |
| URI: | http://repository.its.ac.id/id/eprint/78683 |
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