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

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

doi:10.5121/ijcnc.2023.15101

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

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

Department of Computer and Network Engineering

Research output: Contribution to journal › Article › peer-review

9 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 language	English
Journal	International Journal of Computer Networks and Communications
Volume	15
Issue number	1
DOIs	https://doi.org/10.5121/ijcnc.2023.15101
Publication status	Published - 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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10.5121/ijcnc.2023.15101

Cite this

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AB - 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).

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

Abstract

Keywords

ASJC Scopus subject areas

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