Deep learning based identification of DDoS attacks in industrial application

被引：0

作者：

Bhati, Akhilesh ^{[1
]}

Bouras, Abdelaziz ^{[1
]}

Qidwai, Uvais Ahmed ^{[1
]}

Belhi, Abdelhak ^{[1
]}

机构：

[1] Qatar Univ, Coll Engn, Comp Sci & Engn, Doha, Qatar

来源：

PROCEEDINGS OF THE 2020 FOURTH WORLD CONFERENCE ON SMART TRENDS IN SYSTEMS, SECURITY AND SUSTAINABILITY (WORLDS4 2020) | 2020年

关键词：

DDoS attack; Machine learning; Deep defense; Deep learning; ISCX2017; CICIMoS2019; datasets; network security; Industrial Application;

D O I：

10.1109/worlds450073.2020.9210320

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：190 / 196

页数：7

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