AN INTRUSION DETECTION MECHANISM FOR MANETS BASED ON DEEP LEARNING ARTIFICIAL NEURAL NETWORKS (ANNS)

Mohamad T. Sultan, Hesham El Sayed, Manzoor Ahmed Khan

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Mobile Ad-hoc Network (MANET) is a distributed, decentralized network of wireless portable nodes connecting directly without any fixed communication base station or centralized administration. Nodes in MANET move continuously in random directions and follow an arbitrary manner, which presents numerous challenges to these networks and make them more susceptible to different security threats. Due to this decentralized nature of their overall architecture, combined with the limitation of hardware resources, those infrastructure-less networks are more susceptible to different security attacks such as black hole attack, network partition, node selfishness, and Denial of Service (DoS) attacks. This work aims to present, investigate, and design an intrusion detection predictive technique for Mobile Ad hoc networks using deep learning artificial neural networks (ANNs).

Original languageEnglish
JournalInternational Journal of Computer Networks and Communications
Volume15
Issue number1
DOIs
Publication statusPublished - Jan 2023

Keywords

  • ANN
  • Network Protocols
  • deep learning
  • intrusion detection

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Networks and Communications

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