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An Adaptive Dropout Artificial Neural Network-Based Approach For Detecting Version Number Attacks In Rpl-Based IOT Network

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dc.contributor.authorAlfriehat, Nadia Adnan Abdallah
dc.date.accessioned2026-05-11T02:27:52Z
dc.date.available2026-05-11T02:27:52Z
dc.date.issued2025-07
dc.description.abstractThis study introduces an adaptive dropout artificial neural network-based approach, ADAN2_VN, for the detection of VN attacks in RPL-based IoT environments. The proposed framework is structured into four phases: (1) extraction of novel features using statistical analysis of IoT traffic data; (2) data preprocessing encompassing cleansing, balancing, and normalizati on; (3) ensemble feature selection to isolate the most significant attributes; and (4) implementation of an adaptive dropout strategy within an artificial neural network to enhance detection performance.
dc.identifier.urihttps://erepo.usm.my/handle/123456789/24164
dc.language.isoen
dc.subjectInternet of things
dc.subjectComputer networks
dc.titleAn Adaptive Dropout Artificial Neural Network-Based Approach For Detecting Version Number Attacks In Rpl-Based IOT Network
dc.typeResource Types::text::thesis::doctoral thesis
dspace.entity.typePublication
oairecerif.author.affiliationUniversiti Sains Malaysia
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