Adaptive Decision Tree With Random Forest Integration And Dimensionality Reduction For Efficient Botnet Forensics

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dc.contributor.authorAlzubaidi, Ahmed Najm Obaid
dc.contributor.authorIbrahim, Abdullahi Abdu
dc.contributor.authorAbdulrazzaq, Mohammed Majid
dc.date.accessioned2025-10-22T11:39:58Z
dc.date.available2025-10-22T11:39:58Z
dc.date.issued2025
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Bilişim Teknolojileri Ana Bilim Dalı
dc.description.abstractAdaptive Decision Tree with Random Forest Integration and Dimensionality Reduction for Efficient Botnet Forensics proposes a new method for botnet detection by combining an adaptive decision tree with random forest integration. As response variables are highly dimensioned in botnet detection, multistep and time-consuming detection processes are major challenges. With an integrated method, we could first model the response variables as multiclass and regression formats to simplify the construction of decision trees. Then, based on the adaptive decision tree feature selection, we filter out non-obvious features to efficiently establish the random forest regression model under the appropriately sized feature space. Furthermore, to handle multilabel classification, a random forest is employed as the global model in Tree-2-Rule procedures to detect botnet-affected communication behaviors. Finally, real data experiments have been conducted based on the top datasets. The results show that the adaptive decision tree has excellent improvements in efficiency and accuracy. In future research, the GPU ninthordinal censored multistate diagnosis data is useful observed materials that do not destroy the random effect influence of the data. Also, whether the application of the random forest model could save more time in analyzing existing commercial status is an issue to be clarified in future development. Additionally, the development of the method proposed in this research requires further investigation. We could improve and then propose the preferable solution based on the research results. Our proposed solution can be utilized as a tool for efficient real-time bot incident investigations, in accordance with both academic and business objectives.
dc.identifier.citationAlzubaidi, A. N. O., Ibrahim, A. A., & Abdulrazzaq, M. M. (2025, February). Adaptive Decision Tree With Random Forest Integration And Dimensionality Reduction For Efficient Botnet Forensics. In 2025 1st International Conference on Secure IoT, Assured and Trusted Computing (SATC) (pp. 1-12). IEEE. 10.1109/SATC65530.2025.11137233
dc.identifier.doi10.1109/SATC65530.2025.11137233
dc.identifier.isbn9798331514204
dc.identifier.scopus2-s2.0-105017968627
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5977
dc.indekslendigikaynakScopus
dc.institutionauthorAlzubaidi, Ahmed Najm Obaid
dc.institutionauthorIbrahim, Abdullahi Abdu
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2025 IEEE 1st Secure and Trustworthy Cyberinfrastructure for IoT and Microelectronics, SATC 2025 - Conference Proceedings
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Öğrenci
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectBOTNETS
dc.subjectDDOS
dc.subjectDT
dc.subjectIOT
dc.subjectML
dc.subjectRF
dc.titleAdaptive Decision Tree With Random Forest Integration And Dimensionality Reduction For Efficient Botnet Forensics
dc.typeConference Object

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