Open Access
Issue |
MATEC Web Conf.
Volume 392, 2024
International Conference on Multidisciplinary Research and Sustainable Development (ICMED 2024)
|
|
---|---|---|
Article Number | 01141 | |
Number of page(s) | 8 | |
DOI | https://doi.org/10.1051/matecconf/202439201141 | |
Published online | 18 March 2024 |
- K. Bai, A. Zhang, Z. Li, R. Heano, C. Wang, L. Carin. Collaborative Anomaly Detection. arXiv preprint arXiv:2209.09923, (2022) [Google Scholar]
- H. Tahaei, F. Afifi, A. Asemi, F. Zaki, N.B. Anuar. The rise of traffic classification in IoT networks: A survey. J Netw Comput Appl., 154, (2020) [Google Scholar]
- S. Hajiheidari, K. Wakil, M. Badri, N.J. Navimipour. Intrusion detection systems in the Internet of things: A comprehensive investigation. Computer Networks, 160, 165-191, (2019) [CrossRef] [Google Scholar]
- M.G. Samaila, M. Neto, D.A. Fernandes, M.M. Freire, P.R. Inácio. Challenges of securing Internet of Things devices: A survey. Security and Privacy, 1, 2, (2018) [CrossRef] [Google Scholar]
- S. Bhattarai, Y. Wang. End-to-end trust and security for Internet of Things applications. Computer, 51, 4, 20-27, (2018) [CrossRef] [Google Scholar]
- A.M. Chu, M.K. So. Organizational information security management for sustainable information systems: An unethical employee information security behavior perspective. Sustainability, 12, 8, (2020) [Google Scholar]
- V. Ponnusamy, M. Humayun, N.Z. Jhanjhi, A. Yichiet, M.F. Almufareh. Intrusion Detection Systems in Internet of Things and Mobile Ad-Hoc Networks. Comput Syst Sci Eng., 40, 3, 1199-1215, (2022) [CrossRef] [Google Scholar]
- S. Alharbi, D. Alghazzawi, A. Hakeem, L. Mohaisen, L. Cheng, A. Attiah. A Blockchain-Based Collaborative Intrusion Detection Systems Framework. IEEE Internet Things J., (2023) [Google Scholar]
- S. Ali, Q. Li, A. Yousafzai. Blockchain and federated learning-based intrusion detection approaches for edge-enabled industrial IoT networks: A survey. Ad Hoc Netw., 152, (2024) [Google Scholar]
- L. Cui, Y. Qu, G. Xie, D. Zeng, R. Li, S. Shen, S. Yu. Security and privacy-enhanced federated learning for anomaly detection in IoT infrastructures. IEEE Trans. Ind. Inform., 18, 5, 3492-3500, (2021) [Google Scholar]
- Y. Wei, L. Liang, B. Zhou, X. Feng. A modified blockchain DPoS consensus algorithm based on anomaly detection and reward-punishment. In 13th International Conference on Communication Software and Networks (ICCSN), 283-288, (2021) [Google Scholar]
- W. Li, C. Stidsen, T. Adam. A blockchain-assisted security management framework for collaborative intrusion detection in smart cities. Comput. Electr. Eng., 111, (2023) [Google Scholar]
- Á.J. Varela-Vaca, R.M. Gasca, D. Iglesias, J.M. Gónzalez-Gutiérrez. Automated trusted collaborative processes through blockchain & IoT integration: The fraud detection case. Internet of Things, (2024) [Google Scholar]
- Z. Abou El Houda, H. Moudoud, B. Brik, L. Khoukhi. Blockchain-Enabled Federated Learning for Enhanced Collaborative Intrusion Detection in Vehicular Edge Computing. IEEE Trans. Intell. Transp. Syst., (2024) [Google Scholar]
- O. Alkadi, N. Moustafa, B. Turnbull, K.K.R. Choo. A deep blockchain frameworkenabled collaborative intrusion detection for protecting IoT and cloud networks. IEEE Internet Things J., 8, 12, 9463-9472, (2020) [Google Scholar]
- N. Koroniotis, N. Moustafa, E. Sitnikova, B. Turnbull. Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset. Future Gener. Comput. Syst., 100, 779-796, (2019) [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.