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| Titre: | Securing IoT networks using Machine Learning |
| Auteur(s): | Bouguetaia, Akram Abdelghafour
Seggane, Achraf Ramouane |
| Mots-clés: | Internet of Things (IoT),
Intrusion Detection System (IDS),
Convolutional Neural Network (CNN),
Machine Learning
Random Forest (RF) |
| Date de publication: | 2025 |
| Résumé: | The Internet of Things (IoT) network represents a dynamic yet vulnerable environment,
exposed to numerous cybersecurity threats. This thesis proposes an effective solution
for intrusion detection in IoT using machine learning techniques. Two supervised models,
Random Forest (RF) and Convolutional Neural Network (CNN), are developed and
evaluated using the UNSW-NB15 dataset from the Kaggle platform. Experimental results
show that the RF model offers better accuracy (98tion time, making it suitable for
real-time applications. On the other hand, the CNN model provides good performance
(95RF model is then integrated into a simulated IoT environment via Node-RED, with
real-time alerts delivered through a WhatsApp bot. This architecture demonstrates
the feasibility of an intelligent, lightweight, and effective intrusion detection system for
protecting connected devices. |
| URI/URL: | http://dspace.univ-setif.dz:8888/jspui/handle/123456789/5727 |
| Collection(s) : | Mémoires de master
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