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All Journal Proceeding of the Electrical Engineering Computer Science and Informatics
Sharipuddin, Sharipuddin
STIKOM Dinamika Bangsa
Computer Science & IT Electrical & Electronics Engineering

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Features Extraction on IoT Intrusion Detection System Using Principal Components Analysis (PCA) Sharipuddin, Sharipuddin; Purnama, Benni; Kurniabudi, Kurniabudi; Winanto, Eko Arip; Stiawan, Deris; Hanapi, Darmawijoyo; Idris, Mohd. Yazid; Budiarto, Rahmat
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2098

Abstract

There are several ways to increase detection accuracy result on the intrusion detection systems (IDS), one way is feature extraction. The existing original features are filtered and then converted into features with lower dimension. 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.
Co-Authors Darmawijoyo, Darmawijoyo Deris Stiawan Idris, Mohd. Yazid Kurniabudi, Kurniabudi Purnama, Benni Yundari, Yundari