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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Teknologi Informasi dan Ilmu Komputer Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika (JUTIF) Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Jurnal Manajemen Sistem Informasi Bulletin of Informatics and Data Science
Sharipuddin, Sharipuddin
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Journal : Jurnal Teknik Informatika (JUTIF)

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RNN-Based Intrusion Detection System for Internet of Vehicles with IG, PCA, and RF Feature Selection Purnama, Benni; Winanto, Eko Arip; Sharipuddin, Sharipuddin; Sandra, Dodi; Nurhadi, Nurhadi; Afuan, Lasmedi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5293

Abstract

Cyberattacks in the Internet of Vehicles (IoV) threaten road safety and data integrity, requiring intrusion detection systems (IDS) that capture temporal patterns in vehicular traffic. This study develops a Recurrent Neural Network (RNN)-based IDS and evaluates three feature-selection strategies—Information Gain (IG), Principal Component Analysis (PCA), and Random Forest (RF)—on the CICIoV2024 dataset. Features are normalized using Min–Max scaling before being fed into the RNN classifier. The models achieve perfect classification on held-out tests (accuracy/precision/recall/F1 = 1.00). However, probabilistic evaluation reveals low ROC–AUC scores (IG: 0.572, PCA: 0.429, RF: 0.415), indicating limited discriminative margins and potential overfitting or calibration issues despite flawless confusion matrices. PCA and RF further reduce computational overhead during inference compared to IG. These findings highlight that relying solely on accuracy can be misleading for IDS evaluation; temporal RNNs should be complemented with probability-aware training, calibration, or hybrid architectures. This work contributes a temporal-aware IDS framework for IoV and motivates future research on real-time deployment, hybrid RNN-CNN/LSTM models, and adversarial robustness to improve generalization and safety of connected vehicles
Co-Authors Dedi Saputra Dodi Sandra Farista R, Rahul Febriani ER, Riri Jasmir, Jasmir Kurniabudi, Kurniabudi Lasmedi Afuan Lola Yorita Astri, Lola Yorita Nanda Putra, Yofi NOVIANTO, YUDI Nurhadi Pareza Alam Jusia, Pareza Alam Pratama, Bimo Purnama, Benni Rizki Anggraini, Ayu Syaputra, Dikky Takhir, Said Hambali WIJAYA, IBNU SANI