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All Journal Journal of Computing Theories and Applications Journal of Future Artificial Intelligence and Technologies
Bentaleb, Youssef
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Computer Science & IT Decision Sciences, Operations Research & Management

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Journal : Journal of Computing Theories and Applications

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A Comprehensive Approach to Protocols and Security in Internet of Things Technology Ntayagabiri, Jean Pierre; Bentaleb, Youssef; Ndikumagenge, Jeremie; EL Makhtoum, Hind
Journal of Computing Theories and Applications Vol. 2 No. 3 (2025): JCTA 2(3) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.11660

Abstract

The exponential growth of the Internet of Things (IoT) introduces a multitude of security challenges, as a vast number of connected devices often operate with inadequate protection measures. This vulnerability heightens the risk of cyberattacks, data breaches, and hacking, exposing systems and sensitive information to increased threats. Ensuring security in the IoT ecosystem while considering this rapidly expanding technology's physical limitations and specific requirements is a complex task. This article comprehensively analyzes the primary vulnerabilities and risks associated with IoT, exploring innovative strategies and effective solutions to strengthen its security framework. The article highlights the critical role of secure device authentication, data encryption, regular updates, and continuous monitoring by addressing the intricacies of communication protocols and emphasizing the need for standardization. Ultimately, this work advocates for a holistic approach to IoT security, where robust, adaptable solutions are developed to safeguard against the evolving landscape of cyber threats.
A Comparative Analysis of Supervised Machine Learning Algorithms for IoT Attack Detection and Classification Ntayagabiri, Jean Pierre; Bentaleb, Youssef; Ndikumagenge, Jeremie; El Makhtoum, Hind
Journal of Computing Theories and Applications Vol. 2 No. 3 (2025): JCTA 2(3) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.11901

Abstract

The proliferation of Internet of Things (IoT) devices has introduced significant security challenges, necessitating robust attack detection mechanisms. This study presents a comprehensive comparative analysis of ten supervised learning algorithms for IoT attack detection and classification, addressing the critical challenge of balancing detection accuracy with practical deployment constraints. Using the CICIoT2023 dataset, encompassing data from 105 IoT devices and 33 attack types, we evaluate Naive Bayes, Artificial Neural Networks (ANN), Logistic Regression (LR), k-NN, XGBoost, Random Forest (RF), LightGBM, GRU, LSTM, and CNN algorithms based on some performance metrics. The comparative test results show superior performance to the traditional ensemble approach, with RF achieving 99.29% accuracy and leading precision (82.30%), followed closely by XGBoost with 99.26% accuracy and 79.60% precision. Deep learning approaches also demonstrate strong capabilities, with CNN achieving 98.33% accuracy and 71.18% precision, though these metrics indicate ongoing challenges with class imbalance. The analysis of confusion matrices reveals varying success across different attack types, with some algorithms showing perfect detection rates for certain attacks while struggling with others. The study highlights a crucial distinction in IoT security: while high precision remains important, the potentially catastrophic impact of missed attacks necessitates equal attention to recall metrics, as evidenced by the varying recall rates across algorithms (RF: 72.19%, XGBoost: 71.69%, CNN: 64.72%). These findings provide vital insights for developing balanced, context-aware intrusion detection systems for IoT environments, emphasizing the need to consider performance metrics and practical deployment constraints.
Co-Authors EL Makhtoum, Hind Ndikumagenge, Jeremie Ntayagabiri, Jean Pierre