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All Journal JR : Jurnal Responsive Teknik Informatika Jurnal Responsive Teknik Informatika
Nur Halizzah
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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IMPROVEMENT OF IOT SECURITY WITH A MACHINE LEARNING BASED INTRUSION DETECTION SYSTEM APPROACH Nur Halizzah; Indah Kusuma Dewi; Atman Lucky Fernandes; David Saro
Jurnal Responsive Teknik Informatika Vol 8 No 01 (2024): JR : Jurnal Responsive Teknik Informatika
Publisher : LPPM Universitas Ibnu Sina Batam

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Abstract

The development of the Internet of Things (IoT) has brought convenience to variousaspects of life, but it also presents significant challenges regarding cybersecurity. One solutionto address this issue is the development of an Intrusion Detection System (IDS) based on machinelearning. This study aims to design an efficient and adaptive IDS for IoT environments usingmachine learning algorithms such as Random Forest and Support Vector Machine (SVM). Themethodology includes system design, data collection, algorithm selection, model training, andsystem performance evaluation. The results show that Random Forest and SVM algorithms areeffective in detecting attacks such as Distributed Denial of Service (DDoS) and malware, with arelatively high accuracy rate. However, the main challenges faced are the need for representativedatasets and computational efficiency issues on resource-constrained IoT devices. This studyconcludes that machine learning-based intrusion detection systems can improve IoT security byaccurately detecting cyber-attacks. Further development is expected to address efficiencyconstraints and enhance the system's reliability in facing increasingly complex threats.
IMPROVEMENT OF IOT SECURITY WITH A MACHINE LEARNING BASED INTRUSION DETECTION SYSTEM APPROACH Nur Halizzah; Indah Kusuma Dewi; Atman Lucky Fernandes; David Saro
Jurnal Responsive Teknik Informatika Vol 8 No 01 (2024): JR : Jurnal Responsive Teknik Informatika
Publisher : LPPM Universitas Ibnu Sina Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The development of the Internet of Things (IoT) has brought convenience to variousaspects of life, but it also presents significant challenges regarding cybersecurity. One solutionto address this issue is the development of an Intrusion Detection System (IDS) based on machinelearning. This study aims to design an efficient and adaptive IDS for IoT environments usingmachine learning algorithms such as Random Forest and Support Vector Machine (SVM). Themethodology includes system design, data collection, algorithm selection, model training, andsystem performance evaluation. The results show that Random Forest and SVM algorithms areeffective in detecting attacks such as Distributed Denial of Service (DDoS) and malware, with arelatively high accuracy rate. However, the main challenges faced are the need for representativedatasets and computational efficiency issues on resource-constrained IoT devices. This studyconcludes that machine learning-based intrusion detection systems can improve IoT security byaccurately detecting cyber-attacks. Further development is expected to address efficiencyconstraints and enhance the system's reliability in facing increasingly complex threats.
Co-Authors David Saro Fernandes, Atman Lucky Indah Kusuma Dewi, Indah Kusuma