Proceeding of the Electrical Engineering Computer Science and Informatics
Vol 7, No 1: EECSI 2020

Features Extraction on IoT Intrusion Detection System Using Principal Components Analysis (PCA)

Sharipuddin Sharipuddin (STIKOM Dinamika Bangsa)
Benni Purnama (STIKOM Dinamika Bangsa)
Kurniabudi Kurniabudi (STIKOM Dinamika Bangsa)
Eko Arip Winanto (Uiversiti Teknologi Malaysia)
Deris Stiawan (University of Sriwijaya)
Darmawijoyo Hanapi (University of Sriwijaya)
Mohd. Yazid Idris (Uiversiti Teknologi Malaysia)
Rahmat Budiarto (Al Baha University)

Article Info

Publish Date
23 Nov 2020

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.

Copyrights © 2020




Citation Download
RIS
EndNote, Reference Manager, ProCite
BibTex
Latex, Jabref
Original Source
Download Original
Google Scholar
Check in Google Scholar
Journal Info
Proceeding of the Electrical Engineering Computer Science and Informatics
Website

Abbrev

EECSI

Publisher

Institute of Advanced Engineering and Science

Subject

Computer Science & IT Electrical & Electronics Engineering

Description

Proceeding of the Electrical Engineering Computer Science and Informatics publishes papers of the "International Conference on Electrical Engineering Computer Science and Informatics (EECSI)" Series in high technical standard. The Proceeding is aimed to bring researchers, academicians, scientists, ...