Abstract
In today’s digital landscape, Artificial Intelligence (AI) plays a crucial role in addressing cybersecurity challenges faced by IT companies, as the threat of distributed attacks persists despite implementing Network Intrusion Detection Systems (NIDSs). We propose a novel hybrid classifier leveraging distributivity equations to combine k-Nearest Neighbors (kNN), Decision Trees (DT), and Stochastic Gradient Descent (SGD). Evaluated on UNSW-NB15 and SIMARGL2021 datasets, our method demonstrates competitive performance in accuracy, recall, precision, F1-score, and area under ROC curve (AUC) compared to base classifiers and SOTA techniques (Stacking, Soft Voting - Weighted Average Probabilities, Adaptive Boosting (AdaBoost) and Histogram-based Gradient Boosting Classification Tree (HGBC)). Key innovations include a distributivity-based aggregation framework and class-balancing strategy for imbalanced datasets.
Paper Type
Short Paper
DOI
10.62036/ISD.2025.41
Detection of abnormal network flow by the Distributive Aggregations Ensemble Algorithm
In today’s digital landscape, Artificial Intelligence (AI) plays a crucial role in addressing cybersecurity challenges faced by IT companies, as the threat of distributed attacks persists despite implementing Network Intrusion Detection Systems (NIDSs). We propose a novel hybrid classifier leveraging distributivity equations to combine k-Nearest Neighbors (kNN), Decision Trees (DT), and Stochastic Gradient Descent (SGD). Evaluated on UNSW-NB15 and SIMARGL2021 datasets, our method demonstrates competitive performance in accuracy, recall, precision, F1-score, and area under ROC curve (AUC) compared to base classifiers and SOTA techniques (Stacking, Soft Voting - Weighted Average Probabilities, Adaptive Boosting (AdaBoost) and Histogram-based Gradient Boosting Classification Tree (HGBC)). Key innovations include a distributivity-based aggregation framework and class-balancing strategy for imbalanced datasets.
Recommended Citation
Rak, E., Sarzyński, J. & Homa, M. (2025). Detection of abnormal network flow by the Distributive Aggregations Ensemble AlgorithmIn I. Luković, S. Bjeladinović, B. Delibašić, D. Barać, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Empowering the Interdisciplinary Role of ISD in Addressing Contemporary Issues in Digital Transformation: How Data Science and Generative AI Contributes to ISD (ISD2025 Proceedings). Belgrade, Serbia: University of Gdańsk, Department of Business Informatics & University of Belgrade, Faculty of Organizational Sciences. ISBN: 978-83-972632-1-5. https://doi.org/10.62036/ISD.2025.41