2026-10-08T23:45:14.463Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:2:12173 2026-09-10T23:34:21Z 2

Detecting click fraud instances using modified metaheuristic optimized recurrent networks 2026 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/2/12173 https://link.springer.com/article/10.1007/s10586-026-06493-z N. Bacanin Z. Spalevic R. Dragic D. Markovic L. Babic I. Milovanovic L. Jovanovic M. Zivkovic P. Spalevic In the digital age, online businesses often rely on advertising as a primary source of revenue. Many services and platforms depend on advertisement income to remain operational, allowing free services to be sustained through ad funding. Advertisers typically pay per click, with fees determined by click-through rates. However, a major challenge in this system is click fraud, a deceptive practice in which advertisements are repeatedly clicked without genuine interest or intent to make a purchase. This behavior leads to financial losses for advertisers, damages platform reputations, and distorts the effectiveness of online advertising. Detecting fraudulent clicks remains a complex challenge due to the constantly evolving tactics employed by fraudsters. This study explores the potential of artificial intelligence (AI) classifiers for identifying instances of click fraud. Because the performance of AI-based classifiers strongly depends on proper hyperparameter selection, a modified optimization metaheuristic is introduced to improve classification accuracy. A comparative analysis is conducted using real-world data, demonstrating promising results from the best-performing models with accuracy as high as 0.786496. Finally, the legal and policy implications of such a system are examined within the context of an evolving digital landscape. article 10.1007/s10586-026-06493-z 29 724 1386-7857 Cluster Computing