• E Hakizimana
  • G Dik
  • O Gervasi (Editor)
  • B Murgante (Editor)
  • C Garau (Editor)
  • Y Karaca (Editor)
  • MNF Lago (Editor)
  • F Scorza (Editor)
  • AC Braga (Editor)
Our main area of study is IoT security, including its effects and ways to mitigate them in real time. This study focuses on Distributed Denial-of-Service (DDoS) assaults, which pose a serious risk to Internet of Things networks because of the growing number of unprotected devices. We examine the characteristics of DDoS attacks, examine the strategies to detect it, especially in the African setting, and suggest a detection methodology based on algorithms from Adaptive Resonance Theory (ART) and Long Short-Term Memory (LSTM). The detection performance is evaluated using metrics such as F1-score, precision, and recall. By offering an efficient DDoS detection framework, the study helps to improve real-time IoT data security.
Original languageEnglish
Title of host publicationCOMPUTATIONAL SCIENCE AND ITS APPLICATIONS-ICCSA 2025 WORKSHOPS, PT XIII
PublisherSpringer Nature
Pages364-374
Number of pages11
ISBN (Print)978-3-031-97656-8, 978-3-031-97657-5
DOIs
StatePublished - 2026
EventComputational Science and Its Applications – ICCSA 2025 Workshops - Стамбул, Turkey
Duration: 30 Jun 20253 Jul 2025
http://iccsa.org

Publication series

NameLecture Notes in Computer Science
Volume15898 LNCS

Conference

ConferenceComputational Science and Its Applications – ICCSA 2025 Workshops
Abbreviated titleICCSA
Country/TerritoryTurkey
CityСтамбул
Period30/06/253/07/25
Internet address

    Research areas

  • Distributed Denial-of-Service (DDoS), Real-time Detection, Long Short-Term Memory (LSTM), Adaptive Resonance Theory (ART), Machine Learning, Cybersecurity, Network Security, Africa

ID: 151949096