RNN-based DDoS Detection in IoT Scenario

Chun Yu Chen, Lo An Chen, Yun Zhan Cai, Meng Hsun Tsai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With the advancement of wired and wireless communication technologies, the Internet of Things (IoT) devices are also increasing. Hackers exploit a massive amount of IoT devices, which lack security protection for specific purposes. Distributed denial of service (DDoS) attack is an enhanced denial of service (DoS) attack and is one of these hacked devices' common usages. This paper proposes a time-stamped bi-directional gated recurrent unit (GRU) model to detect DDoS attacks. Compared with previous work, our method maintains higher accuracy and lower training time. Generally, in most DDoS attack schemes, the accuracy is still high.

Original languageEnglish
Title of host publicationProceedings - 2020 International Computer Symposium, ICS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages448-453
Number of pages6
ISBN (Electronic)9781728192550
DOIs
Publication statusPublished - 2020 Dec
Event2020 International Computer Symposium, ICS 2020 - Tainan, Taiwan
Duration: 2020 Dec 172020 Dec 19

Publication series

NameProceedings - 2020 International Computer Symposium, ICS 2020

Conference

Conference2020 International Computer Symposium, ICS 2020
CountryTaiwan
CityTainan
Period20-12-1720-12-19

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management
  • Computational Mathematics

Fingerprint Dive into the research topics of 'RNN-based DDoS Detection in IoT Scenario'. Together they form a unique fingerprint.

Cite this