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

Alfriehat, Nadia Adnan Abdallah (2025) An Adaptive Dropout Artificial Neural Network-Based Approach For Detecting Version Number Attacks In Rpl-Based Iot Network. PhD thesis, Universiti Sains Malaysia.

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Abstract

This 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.

Item Type: Thesis (PhD)
Subjects: T Technology > T Technology (General) > T1-995 Technology(General)
Divisions: Pusat IPv6 Termaju Negara (National Advanced IPv6 Centre of Excellence NAv6) > Thesis
Depositing User: Mr Aizat Asmawi Abdul Rahim
Date Deposited: 11 May 2026 02:35
Last Modified: 11 May 2026 02:35
URI: http://eprints.usm.my/id/eprint/64142

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