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Lukman Hakim
Universitas Yudharta Pasuruan
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Other

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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.
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 Achmad Afifuddin Adrianto, Dian Agus Prianto Ahfa, Mokhamad Isna Marzuki Ahwan, Zainul Ainiyah, Roisatul Aliansyah, Zulhaj Amang Fathurrohman Amang Fathurrohman Amelia, Resti Aminatuz Zuhriyah Aminatuz Zuhriyah As'idah, Fizriyatul Awang Rivaldo Bachtiar, Mochammad Alvin Tri Baehaqi Bagus Hari Sugiharto Bagus Hari Sugiharto Baroah, Hamida Maulana Lailatal Cahya Bagus Sanjaya Candra Adi Lesmana Deby Rizky Prihatini E Ernawati, E Ernawati Evy Kamilah Ratnasari Fafit Rahmat Aji Firda Nur Aisyah Firdaus, Lukman Fitrianawati, Meita Hapsari Titi Palupi Imam Syafi’i Imam Wahyudi Farid Irwan Abdullah Khoilil Fitria Kholid Murtadlo Lilis Trisnawati M Dayat M. Daimul Abror M. Ikmal Farih Miftahul Huda Miftahul Huda Misbach Munir Moch. Lutfi Mochamad Hasyim Mohamad Zein Saleh Muh. Aniar Hari Swasono Muhammad Fahmy Mayadi Muhammad Faishol Amrulloh Muhammad Imron Rosadi Muhammad Imron Rosadi Mulyono Wobisono Nabilah, Lailatun Novitasari, Tri Wahyu Nur Aisyah, Firda Nur Robiah Nofikusumawati Peni Nurus Sa'adah Okariawan, I.D.K. Prayoga, Resdi Hadi Prayoga, Resdi Hadi Rahayu, Silvia Indah Resdi Hadi Prayoga Ridha, Faishal Ananta Roisatul Ainiyah Seviya, Trisna Siti Mutrofin Sugiyarto Sugiyarto Sujita, Sujita Sulhan, M. Syarwani, Muhammad Syukur Sugeng Apriwiyanto Tiara Meylinda S Widodo S Pranowo Yusebla, Agil