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An Optimized Gradient Boost Decision Tree Using Enhanced African Buffalo Optimization Method for Cyber Security Intrusion Detection

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العنوان: An Optimized Gradient Boost Decision Tree Using Enhanced African Buffalo Optimization Method for Cyber Security Intrusion Detection
المؤلفون: Shailendra Mishra
المصدر: Applied Sciences; Volume 12; Issue 24; Pages: 12591
بيانات النشر: Multidisciplinary Digital Publishing Institute
سنة النشر: 2022
المجموعة: MDPI Open Access Publishing
مصطلحات موضوعية: cyber security, IDS, GA, OGBDT, EABO
جغرافية الموضوع: agris
الوصف: The cyber security field has witnessed several intrusion detection systems (IDSs) that are critical to the detection of malicious activities in network traffic. In the last couple of years, much research has been conducted in this field; however, in the present circumstances, network attacks are increasing in both volume and diverseness. The objective of this research work is to introduce new IDSs based on a combination of Genetic Algorithms (GAs) and Optimized Gradient Boost Decision Trees (OGBDTs). To improve classification, enhanced African Buffalo Optimizations (EABOs) are used. Optimization Gradient Boost Decision Trees (OGBDT-IDS) include data exploration, preprocessing, standardization, and feature ratings/selection modules. In high-dimensional data, GAs are appropriate tools for selecting features. In machine learning techniques (MLTs), gradient-boosted decision trees (GBDTs) are used as a base learner, and the predictions are added to the set of trees. In this study, the experimental results demonstrate that the proposed methods improve cyber intrusion detection for unused and new cases. Based on performance evaluations, the proposed IDS (OGBDT) performs better than traditional MLTs. The performances are evaluated by comparing accuracy, precision, recall, and F-score using the UNBS-NB 15, KDD 99, and CICIDS2018 datasets. The proposed IDS has the highest attack detection rates, and can predict attacks in all datasets in the least amount of time.
نوع الوثيقة: text
وصف الملف: application/pdf
اللغة: English
Relation: Computing and Artificial Intelligence; https://dx.doi.org/10.3390/app122412591
DOI: 10.3390/app122412591
الاتاحة: https://doi.org/10.3390/app122412591
Rights: https://creativecommons.org/licenses/by/4.0/
رقم الانضمام: edsbas.5E974EF9
قاعدة البيانات: BASE
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