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All Journal Jurnal Buana Informatika Jurnal Informatika dan Teknik Elektro Terapan Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal RESISTOR (Rekayasa Sistem Komputer) Explore IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika Jurnal Ilmu Komputer dan Desain Komunikasi Visual Jurnal Pengabdian Al-Ikhlas JATI (Jurnal Mahasiswa Teknik Informatika) Soeropati: Journal of Community Service Jurnal Abdimas Berdaya : Jurnal Pembelajaran, Pemberdayaan dan Pengabdian Masyarakat IICS BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Krisnadana Jurnal Manajemen Informatika & Teknologi SisInfo : Jurnal Sistem Informasi dan Informatika Jurnal Krisnadana Nusantara Journal of Computers and its Applications Kohesi: Jurnal Sains dan Teknologi Jurnal Informatika Polinema (JIP) Journal of Information Technology and Cyber Security Jurnal MediaTIK
Muhammad Imron Rosadi
Prodi Teknik Informatika, Fakultas Teknik, Universitas Yudharta Pasuruan
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Journal : Journal of Information Technology and Cyber Security

 1 
Comparison of Dimensionality Reduction Techniques to Improve Performance and Efficiency of Logistic Regression in Network Anomaly Detection Ahfa, Mokhamad Isna Marzuki; Hakim, Lukman; Rosadi, Muhammad Imron
Journal of Information Technology and Cyber Security Vol. 3 No. 1 (2025): January
Publisher : Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30996/jitcs.12212

Abstract

Network anomaly detection is a crucial process to identify abnormal network traffic, which may pose a security threat. This research aims to improve the performance and efficiency of Logistic Regression (LR) in network anomaly detection by applying dimension reduction techniques, such as Principal Component Analysis (PCA), Truncated Singular Value Decomposition (TSVD), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Independent Component Analysis (ICA). The performance of each dimension reduction method is evaluated based on accuracy, precision, recall, F1-score, and computation time. The results show that TSVD provides the best performance with 95.86% accuracy, 0.96 precision, 0.96 recall, 0.95 F1-score, and 13.83 seconds computation time. In contrast, ICA showed the worst performance, especially in precision, recall, and F1-score, with values of 0.73, 0.83, and 0.78, respectively. Meanwhile, although t-SNE produces competitive accuracy, it has a high computational cost with an execution time of 1698.54 seconds. These findings show that choosing the right dimension reduction algorithm not only improves detection performance but also supports data processing efficiency, making it highly relevant for large-scale network security scenarios. Keywords: dimensionality reduction, Logistic Regression, network anamoly detection, performance evaluation, Truncated Singular Value Decomposition.
Co-Authors ABDUL MAJID Abi Rizal Achmad Badik Irwan Agus Prianto Agus Zainal Arifin Ahfa, Mokhamad Isna Marzuki Ahmad Aminuddin Ahmad Reza Musthafa, Ahmad Reza Ahwan, Zainul Alhaji Sheku Sankoh, Alhaji Sheku Alya Alwiyah Amiruddin Fadli Anggy Jovano Arief Tri Arsanto avysa nabila Bagus Hari Sugiharto Bagus Hari Sugiharto Bahrul Ulum Cahya Bagus Sanjaya Deby Rizky Prihatini Dian Novita Sari Didit Proyogi Dinar Nur Safitri Faizal FARIS FARIS Fera Febriana, Tety Hafilah Farhana Hidayah, Laelatul Irham Maulidi Khoirotul Fitriyah, Khoirotul Khozinul Asror Asror Laili Cahyani Lisda Nursanti LISDA NURSANTI Lukman Hakim Lutfi, Moch. MARIA BINTANG Miftahul Huda Minnatin Charizah Misbahul Munir Moch. Lutfi Moch. Lutfi Mochamad Hasyim Mokhamad Chaidhir Ismail Muhamad Afif Effindi Muhamad Afif Effindi, Muhamad Afif Muhammad Faishol Amrulloh Muhammad Fu’ad Mulyono Wibisono Ninuk Riswandari Nur Faizi Nur Moh Soleh Riska Amelia Rokhmatulloh Selma Karomi Shafira Setiawan Cahyono Siti Muyasaroh Suastika Yulia Riska Trio Arisandi Eko Prayogo Tutik Tutik Walidini Syaihul Huda Widha Pangesti Norcahyani Yaumi, Sri Yuyud Akhmad Dhani