Abstract
The continuous growth of computer networks and the internet has brought attention to the increasing potential damage caused by attacks. Intrusion Detection Systems (IDSs) have emerged as crucial defense tools against the rising frequency and sophistication of network attacks. However, effectively detecting new attacks using machine-learning approaches in intrusion detection systems presents challenges.
This study focuses on the CICIDS2017 dataset, which is one of the most recent and updated IDS datasets publicly available. The CICIDS2017 dataset contains both benign and seven common attack network flows, meeting real-world criteria and providing true network traffic data. Furthermore, The CICIDS2017 dataset presents challenges when it comes to measuring the performance of a comprehensive set of machine learning algorithms in order to identify the optimal pattern set for detecting specific attack categories.
This paper contributes to the field of intrusion detection systems by benchmarking the decision tree family. As a result of our study XGBoost achieves the highest accuracy of 99%, followed by Random Forest with 98%, Gradient Boosting Trees with 88%, and Decision Tree with 89%.
Overall, this research provides valuable insights into the performance of decision tree family and feature selection methods, paving the way for the advancement of more reliable and efficient intrusion detection systems.
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Azalmad, M., El Ayachi, R., Biniz, M. (2023). Unveiling the Performance Insights: Benchmarking Anomaly-Based Intrusion Detection Systems Using Decision Tree Family Algorithms on the CICIDS2017 Dataset. In: El Ayachi, R., Fakir, M., Baslam, M. (eds) Business Intelligence. CBI 2023. Lecture Notes in Business Information Processing, vol 484 . Springer, Cham. https://doi.org/10.1007/978-3-031-37872-0_15
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