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

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:1:11819 2026-01-26T16:07:57Z 1

Modified Metaheuristic Tuning of Reservoir Computing Models for Click Fraud Detection 2025 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/1/11819 https://ieeexplore.ieee.org/abstract/document/11240900 V. Marevic S. Kozakijevic L. Jovanovic M. Zivkovic N. Bacanin V. Zeljkovic Click fraud poses a growing threat to digital advertising ecosystems, impacting advertisers, analysts, and platforms dependent on ad-generated revenue. Traditional countermeasures, such as reCAPTCHA, can mitigate basic automated attacks but may also hinder legitimate user engagement. To address this challenge, this work proposes a novel approach leveraging reservoir computing, specifically E cho S tate N etworks (ESNs), to classify user click sequences and detect fraudulent behavior. Recognizing the critical role of hyperparameter tuning in time series classification, t his work i ntroduces a modified version of the chimp optimization algorithm (ChOA) optimizer to enhance model performance. Experimental results demonstrate that our optimization framework significantly improves detection accuracy, achieving up to.751853 and outperforming standard baseline models. This study highlights the potential of ESNs and tailored optimization strategies for robust, scalable click fraud detection. conferenceObject 38 41 10.1109/TELSIKS65061.2025.11240900 2025 17th International Conference on Advanced Technologies, Systems and Services in Telecommunications (TELSIKS), IEEE, Nis, Serbia