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Deep learning based identification of DDoS attacks in industrial application

المؤلفBhati, Akhilesh
المؤلفBouras, Abdelaziz
المؤلفQidwai, Uvais Ahmed
المؤلفBelhi, Abdelhak
تاريخ الإتاحة2023-04-09T08:34:47Z
تاريخ النشر2020
اسم المنشورProceedings of the World Conference on Smart Trends in Systems, Security and Sustainability, WS4 2020
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.1109/WorldS450073.2020.9210320
معرّف المصادر الموحدhttp://hdl.handle.net/10576/41724
الملخصDenial 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.
راعي المشروعThis 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.
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعCICDDoS2019 datasets
DDoS attack
Deep defense
Deep learning
Industrial Application
ISCX2017
Machine learning
Network security
العنوانDeep learning based identification of DDoS attacks in industrial application
النوعConference Paper
الصفحات190-196
dc.accessType Abstract Only


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