Sharipuddin, Sharipuddin and Purnama, Benni and Kurniabudi, Kurniabudi and Winanto, Eko Arip and Stiawan, Deris and Hanapi, Darmawiiovo and Idris, Mohd. Yazid and Budiarto, Rahmat (2020) Features extraction on iot intrusion detection system using principal components analysis (PCA). In: 7th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2020, 1 - 2 October 2020, Yogyakarta, Indonesia.
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Official URL: http://dx.doi.org/10.23919/EECSI50503.2020.9251292
Abstract
Feature extraction solves the problem of finding the most efficient and comprehensive set of features. A Principle Component Analysis (PCA) feature extraction algorithm is applied to optimize the effectiveness of feature extraction to build an effective intrusion detection method. This paper uses the Principal Components Analysis (PCA) for features extraction on intrusion detection system with the aim to improve the accuracy and precision of the detection. The impact of features extraction to attack detection was examined. Experiments on a network traffic dataset created from an Internet of Thing (IoT) testbed network topology were conducted and the results show that the accuracy of the detection reaches 100 percent.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | Component, Formatting |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Computing |
| ID Code: | 92443 |
| Deposited By: | Widya Wahid |
| Deposited On: | 28 Sep 2021 07:44 |
| Last Modified: | 28 Sep 2021 07:44 |
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