Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
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This Author published in this journals
All Journal Journal of Computing Theories and Applications The Indonesian Journal of Computer Science
Jean Pierre Ntayagabiri
University of Burundi
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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A Comparative Analysis of Supervised Machine Learning Algorithms for IoT Attack Detection and Classification Jean Pierre Ntayagabiri; Youssef Bentaleb; Jeremie Ndikumagenge; Hind El Makhtoum
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.
The Software Development Model–User Satisfaction Gap in Academic Digital Systems: Empirical Evidence from Higher Education Institutions in Goma, DRC Jean Pierre Ntayagabiri; Janvier Twizerimana Sindambiwe; Olivier Baraka Mushage; David Byamungu Wanguwabo; Jeremie Ndikumagenge
The Indonesian Journal of Computer Science Vol. 15 No. 4 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i4.5163

Abstract

The digitalisation of higher education institutions (HEIs) in sub-Saharan African cities presents a para-dox rarely documented in the literature: developers report high satisfaction with their development pro-cesses whilst end-users simultaneously experience low satisfaction with the resulting software products. This article introduces and empirically quantifies the Developer–Stakeholder Understanding Gap (DSU-Gap) — a structural asymmetry between developers’ self-assessed process quality and end-users’ actual product experience — through a tri-group, mixed-methods exploratory study conducted across HEIs in Goma, Democratic Republic of Congo. A total of 175 respondents participated across three groups: 17 software developers, 22 administrative and teaching staff (agent users), and 136 students. Data were collected via a structured questionnaire administered through KoBoToolbox (November 2024–November 2025) and analysed using descriptive statistics, non-parametric inferential tests (Mann-Whitney U, binomial tests), and inductive thematic analysis. Five empirically validated tensions define the DSU-Gap: (T1) Agile is the dominant development model (65%) yet 82% of developers re-port project failures (binomial test: p = .006); (T2) 88% of developers never adapt standard models de-spite 24% acknowledging their inadequacy; (T3) 73% of end-users were never consulted during software conception (p = .026), violating core requirements engineering principles; (T4) 38% of students lack ac-cess to their institution's digital services — an internal digital divide significantly exceeding a 20% threshold (p < .001); and (T5) developers’ process satisfaction is significantly higher than users’ product satisfaction (Mann-Whitney U = 246, p = .007, rank-biserial |r| = 0.45, medium-to-large effect). These findings argue for the design of a contextual software development framework specifically adapted to the operational, financial, and organisational constraints of HEIs in resource-constrained Central Afri-can cities.
Co-Authors David Byamungu Wanguwabo Hind EL Makhtoum Janvier Twizerimana Sindambiwe Jeremie Ndikumagenge Jeremie Ndikumagenge Olivier Baraka Mushage Youssef Bentaleb