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All Journal Prosiding SNATIKA Vol 01 (2011) Telematika Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal CoreIT Applied Information System and Management Technomedia Journal Aptisi Transactions on Technopreneurship (ATT) Building of Informatics, Technology and Science IJIIS: International Journal of Informatics and Information Systems Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) ADI Bisnis Digital Interdisiplin (ABDI Jurnal) Journal of Applied Data Sciences Jurnal Pendidikan dan Teknologi Indonesia International Journal for Applied Information Management Jurnal Abdi Masyarakat Indonesia Journal of Social Research Scientific Journal of Informatics Journal of Digital Market and Digital Currency
Taqwa Hariguna
STMIK AMIKOM Purwokerto
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Public Health Social Sciences

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Optimizing Early Network Intrusion Detection: A Comparison of LSTM and LinearSVC with SMOTE on Imbalanced Data Nugroho, Khabib Adi; Hariguna, Taqwa; Barkah, Azhari Shouni
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.4672

Abstract

This study aims to improve network intrusion detection systems (IDS) by addressing class imbalance in the CICIDS 2017 dataset. It compares the effectiveness of Long Short-Term Memory (LSTM) networks and Linear Support Vector Classifier (LinearSVC) in detecting intrusions, with a focus on the impact of Synthetic Minority Over-sampling Technique (SMOTE) for balancing the dataset. The dataset was preprocessed by removing irrelevant features, handling missing values, and applying Min-Max normalization. SMOTE was applied to balance the training dataset. Results showed that LSTM outperformed LinearSVC, especially in recall and F1-score, after applying SMOTE. This research highlights the benefits of combining LSTM with SMOTE to address class imbalance in IDS and emphasizes the importance of temporal sequence models like LSTM for detecting network intrusions. Future work could involve using the full dataset, exploring advanced feature engineering, and implementing more complex architectures to further enhance performance. This research underscores the critical need for improving network security by addressing the challenges of class imbalance in intrusion detection systems, which is vital for ensuring the real-time identification and mitigation of sophisticated cyber threats in the ever-evolving landscape of network security.
Co-Authors Abdul Azis Abdul Hakim Prima Yuniarto Abidin, Uun Adi Suryaputra Paramita Anton Satria Prabuwono Asikin, Zaenal Azhari Shouni Barkah Baihaqi, Wiga Maulana Bangkit, Jeffri Prayitno Berlilana Berlilana Chen, Shih-Chih Cheng, Tsang-Hsiang Dhanar Intan Surya Saputra Eko Winarto El Emary, Ibrahiem M. M. Fitriani, Radifa Rahma Giat Karyono Hadie, Agus Nur Hananto, Andhika Rafi Harahap, Eka Purnama Heru Supriyanto Hidayat, Muhammad Taufik Nur Husni Teja Sukmana Imam Tahyudin Johan Reimon Batmetan Kim, Jong Il Muflikhatun, Siti Muhamad Yusup NAWANGNUGRAENI, DEVI ASTRI Nazwan, Nazwan Nugroho, Khabib Adi Nurwanti, Aulia Othman, Jalel Ben Pratama, Satrya Fajri Priyadi, Agung PUJI LESTARI Qurotul Aini R Rizal Isnanto Rahayu, Silvia Anggun Ramadani, Nevita Cahaya Riawan, Fendi Rozak, Rofik Abdul Ruangkanjanases, Athapol Salsabila, Salsabila Sarmini Sarmini Setiawan, Ito Subhan Mahendrasyah, Muhammad Sumiati, Ai Irma Susanti, Nindi Nofi Syahrizal, Hendrawan Tikaningsih, Ades Tri Wahyuningsih Uswatun Hasanah Wahid, Arif Mu'amar Waluyo, Retno Yarsasi, Sri Yi, Ding