| Title | Deep Learning Based Identification of DDoS Attacks in Industrial Application |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Bhati, Akhilesh, Bouras, Abdelaziz, Ahmed Qidwai, Uvais, Belhi, Abdelhak |
| Conference Name | 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4) |
| Date Published | July 2020 |
| Publisher | IEEE |
| ISBN Number | 978-1-7281-6823-4 |
| Keywords | Botnet, CICDDoS2019 datasets, composability, Computer crime, DDoS Attack, DDoS Attack Prevention, Deep defense, Deep Learning, feature extraction, Human Behavior, industrial application, ISCX2017, machine learning, machine learning algorithms, Metrics, Network security, pubcrawl, resilience, Resiliency, Servers, telecommunication traffic |
| 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. |
| URL | https://ieeexplore.ieee.org/document/9210320 |
| DOI | 10.1109/WorldS450073.2020.9210320 |
| Citation Key | bhati_deep_2020 |
Deep Learning Based Identification of DDoS Attacks in Industrial Application