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

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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.
OMIC: A Bagging-Based Ensemble Learning Framework for Large-Scale IoT Intrusion Detection Ntayagabiri, Jean Pierre; Bentaleb, Youssef; Ndikumagenge, Jeremie; El Makhtoum, Hind
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 4 (2025): March 2025
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.3048-3719-63

Abstract

The research focuses on developing an Optimized Multiclass Intrusion Classifier (OMIC), an advanced framework for large-scale network intrusion detection in IoT environments. Traditional intrusion detection systems face significant challenges with increasing network complexity, attack sophistication, and the exponential growth of IoT devices, particularly in handling class imbalance, computational efficiency, and real-time processing of massive data volumes. OMIC introduces a novel ensemble approach combining LightGBM and XGBoost classifiers with a memory-optimized processing pipeline to address these limitations. The framework implements sophisticated data handling techniques, including dynamic chunk-based processing, adaptive sampling methods, and cost-sensitive learning to manage class imbalance. Experimental evaluation using the comprehensive CICIoT2023 dataset, comprising over 1 million records and 33 distinct attack types, demonstrates OMIC's exceptional performance with an overall accuracy of 99.26%. The framework achieves perfect precision, recall, and F1-scores for most DDoS and DoS attack categories, significantly outperforming traditional machine learning and deep learning approaches. While excelling in most attack categories, OMIC shows limitations in detecting certain web-based attacks and reconnaissance activities, suggesting areas for future enhancement. The framework's superior performance in handling large-scale data while maintaining high detection accuracy positions it as a significant advancement in IoT network security, offering practical solutions for real-world deployments.
Co-Authors Bentaleb, Youssef EL Makhtoum, Hind Ndikumagenge, Jeremie