Retracted: Intrusion Detection System for IoT Based on Modified Random Forest Algorithm
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Résumé
An intrusion detection system (IDS) is key to having a comprehensive cybersecurity solution against any attack, and artificial intelligence techniques have been combined with all the features of the IoT to improve security. In response to this, in this research, an IDS technique driven by a modified random forest algorithm has been formulated to improve the system for IoT. To this end, the target is made as one-hot encoding, bootstrapping with less redundancy, adding a hybrid features selection method into the random forest algorithm, and modifying the ranking stage in the random forest algorithm. Furthermore, three datasets have been used in this research, IoTID20, UNSW-NB15, and IoT-23. The results are compared with the three datasets mentioned above and it emerges that the accuracy of the proposed system is 96.2%, which is better than the other methods in the IoTID20 Dataset, while the accuracy with the second dataset UNSW-NB15 yielded 98.85%. Lastly, using the third dataset, IoT-23, the suggested technique achieved 99.93%.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
La notice
- Revue
- Iraqi Journal for Computer Science and Mathematics
- Thématique
- Network Security and Intrusion Detection
- Domaine
- Computer Science
- Établissements canadiens
- Artificial Intelligence in Medicine (Canada)
- Organismes subventionnaires
- —
- Mots-clés
- Random forestComputer scienceInternet of ThingsIntrusion detection systemAlgorithmIntrusionArtificial intelligenceGeologyComputer security
- Résumé présent dans OpenAlex
- oui