Intrusion Detection System Using Deep Learning for Mobile Ad-hoc Network

Mulunesh, Matebie

dc.contributor.author	Mulunesh, Matebie
dc.date.accessioned	2024-12-06T07:50:49Z
dc.date.available	2024-12-06T07:50:49Z
dc.date.issued	2023-06
dc.identifier.uri	http://ir.bdu.edu.et/handle/123456789/16306
dc.description.abstract	The popularity of mobile ad hoc networks (MANETs) has grown significantly due to their cheap and ease of deployment. However, they appear to be more vulnerable to various attacks due to the open access medium and the dynamically changing network topology such as denial of service, packet dropping, routing disruption and resource consumption. Intrusion detection system plays an important role to safeguard security in MANETs to monitor network traffic and make a proper analysis of the network. There are various research studies to prevent and detect such types of attacks, however still needs additional studies because of the continuous expansion of network data, a large amount of non-linear network data brings new challenges to intrusion detection. This study aims to preprocess and select the important features to enhance the performance of detection model. We used the Bidirectional long-short term memory (BiLSTM) deep learning algorithm applied on NSL-KDD dataset we have achieved the highest prediction accuracy rate 99.673% and lower false positive rate of 0.0118%.	en_US
dc.language.iso	en_US	en_US
dc.subject	Information Technology	en_US
dc.title	Intrusion Detection System Using Deep Learning for Mobile Ad-hoc Network	en_US
dc.type	Thesis	en_US