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All Journal Jurnal Sistem dan Informatika Jurnal Ilmiah Pangabdhi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JUTIK : Jurnal Teknologi Informasi dan Komputer WIDYABHAKTI Jurnal Ilmiah Populer WIDYA LAKSANA CSRID (Computer Science Research and Its Development Journal) Jurnal Teknologi Informasi dan Pendidikan Jurnal Ilmiah Pendidikan Citra Bakti Madaniya Jurnal Ilmiah Pendidikan Citra Bakti
Made Pradnyana Ambara, Made Pradnyana
Politeknik Negeri Bali
Religion Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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Weighted ANOVA and Mutual Information for Enhanced Intrusion Detection System I Gede Teguh Satya Dharma; I Wayan Rizky Wijaya; I Made Agus Oka Gunawan; Made Pradnyana Ambara
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 1, February 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i1.2448

Abstract

The rapid escalation in the sophistication of network attacks has exposed the limitations of traditional Intrusion Detection Systems (IDS). While machine learning has shown great promise in enhancing IDS performance, its success often hinges on the effectiveness of feature selection. Standard feature selection techniques, however, struggle in cybersecurity applications due to the highly imbalanced nature of network traffic datasets. In such settings, minority attack classes, though critical, are often overshadowed by majority classes, leading to reduced detection of rare intrusions. To address this challenge, we propose a hybrid feature selection framework that integrates Analysis of Variance (ANOVA) and Mutual Information (MI) with a novel class-frequency weighting mechanism. This weighting scheme adjusts the relevance score of each feature according to the distribution of classes, ensuring that features associated with rare attacks are more strongly emphasized during the selection process. We evaluate our method on the UNSW-NB15 dataset using a Support Vector Machine classifier. The results show that our approach achieves substantial gains in recall for underrepresented classes while simultaneously reducing feature dimensionality and maintaining efficiency. By improving the visibility of features tied to minority attacks, the proposed framework provides a more balanced and reliable solution for modern IDS. This contribution advances the detection of rare but impactful threats and highlights a scalable pathway for building more resilient cybersecurity defenses.
Co-Authors Andriati Asri, Sri Antarajaya, I Nyoman Suraja Atmaja, I Made Ari Dwi Suta Elina Rudiastari Evi Triandini Gde Brahupadhya Subiksa Gusti Ngurah Mega Nata Gusti Nyoman Ayu Sukerti I G. N. B. Caturbawa I Gede Teguh Satya Dharma I Made Agus Oka Gunawan I N Satya Kumara I Nyoman Harini Puspita I Nyoman Suraja Antarajaya I Nyoman Suraja Antarajaya I Putu Astya Prayudha I Putu Oka Wisnawa I Wayan Budi Sentana, I Wayan Budi I Wayan Rizky Wijaya Ida Bagus Adisimakrisna Peling Komang Ayu Triana Indah Luh Gede Putri Suardani Made Pasek Agus Ariawan Made Sudarma Made Sudiarta Ni Kadek Sumiari Ni Ketut Pradani Gayatri Sarja Ni Nyoman Harini Puspita Pande Ketut Widiartana Putu Oka Wisnawa Rijal, Muhammad Syamsu Sagung Mas Suryaniadi Sri Andriati Asri, Sri Andriati Sudiartha, I Ketut Gede Yohanes Priyo Atmojo Yudiastra, Putu Pande