| Autor: |
Almaraz-Rivera JG; Tecnologico de Monterrey, School of Engineering and Sciences, Monterrey 64849, Nuevo Leon, Mexico., Cantoral-Ceballos JA; Tecnologico de Monterrey, School of Engineering and Sciences, Monterrey 64849, Nuevo Leon, Mexico., Botero JF; Universidad de Antioquia, Electronics and Telecommunications Engineering Department, GITA-Lab, Medellin 050010, Antioquia, Colombia. |
| Jazyk: |
angličtina |
| Zdroj: |
Sensors (Basel, Switzerland) [Sensors (Basel)] 2023 Oct 25; Vol. 23 (21). Date of Electronic Publication: 2023 Oct 25. |
| DOI: |
10.3390/s23218701 |
| Abstrakt: |
The Internet of Things (IoT), projected to exceed 30 billion active device connections globally by 2025, presents an expansive attack surface. The frequent collection and dissemination of confidential data on these devices exposes them to significant security risks, including user information theft and denial-of-service attacks. This paper introduces a smart, network-based Intrusion Detection System (IDS) designed to protect IoT networks from distributed denial-of-service attacks. Our methodology involves generating synthetic images from flow-level traffic data of the Bot-IoT and the LATAM-DDoS-IoT datasets and conducting experiments within both supervised and self-supervised learning paradigms. Self-supervised learning is identified in the state of the art as a promising solution to replace the need for massive amounts of manually labeled data, as well as providing robust generalization. Our results showcase that self-supervised learning surpassed supervised learning in terms of classification performance for certain tests. Specifically, it exceeded the F1 score of supervised learning for attack detection by 4.83% and by 14.61% in accuracy for the multiclass task of protocol classification. Drawing from extensive ablation studies presented in our research, we recommend an optimal training framework for upcoming contrastive learning experiments that emphasize visual representations in the cybersecurity realm. This training approach has enabled us to highlight the broader applicability of self-supervised learning, which, in some instances, outperformed supervised learning transferability by over 5% in precision and nearly 1% in F1 score. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
|
|
Nepřihlášeným uživatelům se plný text nezobrazuje |
K zobrazení výsledku je třeba se přihlásit.
|
