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All Journal Media Journal of General Computer Science (MJGCS)
Redho Irnindo Saputra
Department of Informatics, Dinamika Bangsa University, Jambi, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Journal : Media Journal of General Computer Science (MJGCS)

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Improvement Attack Detection on Internet of Thinks Using Principal Component Analysis and Random Forest Adrian Pirtama; Yuda Prasetia; Redho Irnindo Saputra; Eko Arip Winanto
Media Journal of General Computer Science Vol. 1 No. 1 (2024): MJGCS
Publisher : MASE - Media Applied and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62205/mjgcs.v1i1.8

Abstract

Network security has become crucial in facing increasingly complex and sophisticated attack threats. Network intrusion detection aids in identifying suspicious activities indicating unauthorized intrusions. This research aims to enhance the performance of advanced attack detection. The Random Forest method is an algorithm that leverages an ensemble of decision trees. This ensemble comprises several independent decision trees used to classify data. One characteristic of the Random Forest method is its ability to address overfitting issues and provide good predictive quality. One approach to improving RF's performance is through Principal Component Analysis (PCA). PCA is a statistical technique used to reduce feature dimensionality. PCA eliminates feature correlations and identifies essential features that can enhance the detection of attacks and normal traffic. This research will be tested with the CIC IoT 2023 dataset, encompassing various attack types. The model testing consists of four feature dimensions, namely 5, 8, 10, and 47. The detection results are promising, significantly improving attack detection performance, reaching up to 99.2%.
Co-Authors Adrian Pirtama Eko Arip Winanto Yuda Prasetia