A Comparative Analysis of Adaboost and XGBoost Meta-Algorthms for improving Network Security
Keywords:
Keywords Algorithm, Dataset, Intrusion, Machine Learning, Models, Network SecurityAbstract
Purpose: This study explores network security, focusing on the application of advanced machine learning algorithms to enhance Intrusion Detection Systems. The increasing frequency of network attacks necessitates robust defense mechanisms and sophisticated methodologies.
Research design, data and methodology: The CRISP-DM framework and a multi-class dataset from the NSL KDD Cup Dataset were used for feature selection and boosting. Adaboost outperformed XGBoost in accuracy, error rate, precision and sensitivity, highlighting the relationship between algorithmic selection and high detection rates in intrusion detection systems. The study emphasizes the importance of considering diverse machine learning models and datasets to refine and advance intrusion detection techniques. The research highlights the evolving landscape of network security and encourages further exploration and integration of various machine learning models and datasets into intrusion detection methodologies.
Conclusions: Real-world implementations are encouraged, focusing on scalability and adaptability. The synergy of advanced machine learning algorithms, meticulous feature selection, and robust methodologies is a promising avenue for fortifying network defenses and ensuring critical system security. This study contributes to the ongoing dialogue on network security, advocating for a proactive approach in refining and implementing intrusion detection systems.
Keywords: Keywords Algorithm, Dataset, Intrusion, Machine Learning, Models, Network Security.
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