2026-05-04T21:56:25.807Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:1:11818 2026-01-26T16:03:57Z 1

Intrusion Detection for Smart City Security by Boosting Algorithms Optimized by Metaheuristics Algorithm 2025 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/1/11818 https://ieeexplore.ieee.org/abstract/document/11240640 N. Bacanin T. Zivkovic M. Zivkovic V. Simic M. Grubjesic A. Kravtsov The flourishing of smart cities has introduced complex and mutually connected cyber-physical systems depending on IoT devices, making them extremely vulnerable to sophisticated cyber attacks. Efficient intrusion detection systems are essential to safeguard these environments, since traditional security solutions fall short in these circumstances. This study explores the integration of XGBoost, a powerful ensemble learning algorithm, with an adapted implementation of a well-known metaheuristic algorithm to improve the detection of network intrusions. Metaheuristics algorithm is employed to fine-tune XGBoost’s hyperparameters, improving classification acuracy and reducing computational overhead. Experimental assessment on benchmark intrusion detection dataset demonstrates that the metaheuristicoptimized XGBoost models significantly outperform baseline models in terms of accuracy, precision, recall, and F1-score. The introduced methodology presents a robust and scalable IDS framework appropriate for the complex and resource-constrained infrastructure of smart cities. conferenceObject 28 33 10.1109/TELSIKS65061.2025.11240640 2025 17th International Conference on Advanced Technologies, Systems and Services in Telecommunications (TELSIKS), IEEE, Nis, Serbia