Deep learning based identification of DDoS attacks in industrial application
| Author | Bhati, Akhilesh |
| Author | Bouras, Abdelaziz |
| Author | Qidwai, Uvais Ahmed |
| Author | Belhi, Abdelhak |
| Available date | 2023-04-09T08:34:47Z |
| Publication Date | 2020 |
| Publication Name | Proceedings of the World Conference on Smart Trends in Systems, Security and Sustainability, WS4 2020 |
| Resource | Scopus |
| Abstract | Denial of Service (DoS) attacks are very common type of computer attack in the world of internet today. Automatically detecting such type of DDoS attack packets dropping them before passing through is the best prevention method. Conventional solution only monitors and provide the feedforward solution instead of the feedback machine-based learning. A Design of Deep neural network has been suggested in this paper. In this approach, high level features are extracted for representation and inference of the dataset. Experiment has been conducted based on the ISCX dataset for year 2017, 2018 and CICDDoS2019 and program has been developed in Matlab R17b using Wireshark. 2020 IEEE. |
| Sponsor | This publication was made possible by NPRP11-S-1227-170135 grant from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. |
| Language | en |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Subject | CICDDoS2019 datasets DDoS attack Deep defense Deep learning Industrial Application ISCX2017 Machine learning Network security |
| Type | Conference Paper |
| Pagination | 190-196 |
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