Show simple item record

AuthorBhati, Akhilesh
AuthorBouras, Abdelaziz
AuthorQidwai, Uvais Ahmed
AuthorBelhi, Abdelhak
Available date2023-04-09T08:34:47Z
Publication Date2020
Publication NameProceedings of the World Conference on Smart Trends in Systems, Security and Sustainability, WS4 2020
ResourceScopus
URIhttp://dx.doi.org/10.1109/WorldS450073.2020.9210320
URIhttp://hdl.handle.net/10576/41724
AbstractDenial 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.
SponsorThis 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.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectCICDDoS2019 datasets
DDoS attack
Deep defense
Deep learning
Industrial Application
ISCX2017
Machine learning
Network security
TitleDeep learning based identification of DDoS attacks in industrial application
TypeConference Paper
Pagination190-196
dc.accessType Abstract Only


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record