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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 : Bulletin of Informatics and Data Science

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Optimizing Autoencoder-Based Feature Selection for Attack Detection in IoT Networks via Machine Learning Approaches Winanto, Eko Arip; Kurniabudi, Kurniabudi; Sharipuddin, Sharipuddin
Bulletin of Informatics and Data Science Vol 3, No 2 (2024): November 2024
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v3i2.104

Abstract

The Internet of Things (IoT) presents significant security challenges as the number of connected devices continues to grow. One critical approach in developing efficient attack detection systems is the selection of relevant features to reduce model complexity without compromising accuracy. This study evaluates the effectiveness of Autoencoders as a feature reduction method for IoT network intrusion detection systems. Three machine learning algorithms are employed for comparative analysis: K-Nearest Neighbors (KNN), Naive Bayes (NB), and Support Vector Machine (SVM). The dataset is evaluated both before and after feature reduction using an Autoencoder, with performance assessed based on accuracy, precision, recall, F1-score, training time, and the number of features. Experimental results demonstrate that the Autoencoder can reduce the number of features by up to 30% without significantly degrading performance. In fact, the NB and SVM models exhibit improvements in both accuracy and training efficiency. The KNN model shows a minimal performance decline, which remains within acceptable limits. Overall, the Autoencoder proves to be an effective method for feature reduction, maintaining or even enhancing detection efficiency and performance. These findings support the use of Autoencoders as an efficient feature selection technique in IoT-based attack detection systems.
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