GRU-Based DDoS Detection for Enhanced Security in Consumer Electronics

Brij B. Gupta, Kwok Tai Chui, Akshat Gaurav, Varsha Arya

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

As IOT devices grow in popularity in the consumer electronics industry, protecting them from cybercriminals has emerged as a top priority. Distributed denial of service (DDoS) attacks, among other cyber threats, pose serious risks to the operation and accessibility of these networked devices. When it comes to complex DDoS attacks, traditional security measures are frequently inadequate, requiring the adoption of cutting-edge machine learning algorithms for detection and mitigation. In this research, we present a new method of DDoS detection based on Gated Recurrent Units (GRUs) to improve security for consumer devices in this setting. To train and check the accuracy of our GRU model, we use the KDD-Cup dataset. With an accuracy of 98%, the proposed GRU-based DDoS detection system performs well in detecting and categorizing DDoS attacks. Our method also has a minimal processing overhead so that it may be used on low-power Internet of Things gadgets.

Original languageEnglish
Title of host publication2023 IEEE 13th International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2023
ISBN (Electronic)9798350324150
DOIs
Publication statusPublished - 2023
Event13th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2023 - Berlin, Germany
Duration: 4 Sept 20225 Sept 2022

Publication series

NameIEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin
ISSN (Print)2166-6814
ISSN (Electronic)2166-6822

Conference

Conference13th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2023
Country/TerritoryGermany
CityBerlin
Period4/09/225/09/22

Keywords

  • Consumer electronics
  • DDoS attack detection
  • GRU (Gated Recurrent Unit)
  • Internet of Things (loT)

Fingerprint

Dive into the research topics of 'GRU-Based DDoS Detection for Enhanced Security in Consumer Electronics'. Together they form a unique fingerprint.

Cite this