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All Journal Jurnal Masyarakat Informatika Jurnal Pendidikan Jasmani Jurnal Simetris Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Information Systems Engineering and Business Intelligence Fountain of Informatics Journal JOIV : International Journal on Informatics Visualization BAREKENG: Jurnal Ilmu Matematika dan Terapan Journal on Education JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Abdimas UNWAHAS : Jurnal Pengabdian Masyarakat UNWAHAS Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran dan Pembelajaran Jurnal Dialektika Informatika (Detika)
Muhammad Malik Hakim, Muhammad Malik
Departemen Informatika, Fakultas Sains Dan Matematika, Universitas Diponegoro
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Transportation Other

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Journal : JOIV : International Journal on Informatics Visualization

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Optimizing Machine Learning Models for Anomaly-based IDS using Intercorrelation Threshold Wahyu Adi, Prajanto; Sugiharto, Aris; Malik Hakim, Muhammad; Rizki Saputra, Naufal; Hanif Setiawan, Syariful
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3355

Abstract

This study aims to improve the performance of attack detection on the Bot-IoT dataset that faces class imbalance. The method used involves developing a feature selection model based on the Pearson correlation coefficient between features, with an adaptive threshold applied. The datasets used consist of two types: D1, with the 10 best features, and D2, with all features. The oversampling technique is applied to the minority class, followed by calculating feature correlations to determine the best feature using a threshold based on the average of the highest and lowest correlations. The feature selection process is carried out iteratively, with performance testing across several machine learning algorithms, including KNN, Random Forest, Logistic Regression, and SVM. The results show that the proposed feature selection method can improve the performance of the minority class without sacrificing the majority class's performance. On the D1 dataset, the Random Forest algorithm achieved 96% accuracy, while KNN achieved 93%. On the D2 dataset, KNN achieved balanced performance, with average precision, recall, and F1-score of 0.99 for both classes, while Random Forest achieved lower results on the minority class. The implications of this study indicate that correlation-based feature selection can improve attack detection performance on datasets with high class imbalance, and it can be implemented in future studies to address similar problems in IoT-based intrusion detection systems.
Co-Authors Ahmad Jazuli Anggraini, Agustina Arief Susanto Aris Sugiharto Ashari, Yeva Fadhilah Choirozaq, Ahmad Dicky Firmansyah Edy Suharto emy siswanah, emy Evanita Evanita, Evanita F. Shoufika Hilyana Fajar Agung Nugroho Farhan Nugraha Pratama Fatullah, Difa Reynikha Fawaiq, Mohammad Nur Fernanda Hendra Priyono Fery Firmansah Fina Fakhriyah Guruh Aryotejo Guruh Aryotejo Guruh Aryotejo, Guruh Hanif Setiawan, Syariful Hendramawat Aski Safarizki Mukhamad Nurkamid RISMIYATI RISMIYATI Rizki Saputra, Naufal Salman Alfarisi Satriyo Adhy Siti Masfuah Siti Maslihah Sonia, Putrisya Novatiara Sutikno Sutikno Wahyu Adi, Prajanto