2026-06-22T06:59:12.161Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:2:11352 2025-03-30T01:38:40Z 2

Click Fraud Detection with Recurrent Neural Networks Optimized by Adapted Crayfish Optimization Algorithm 2024 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/2/11352 https://www.acadlore.com/article/JII/2024_2_4/jii020404 L. Babic V. Zeljkovic L. Jovanovic S. Ivanovic A. Djordjevic M. Zivkovic T. Zivkovic N. Bacanin Click fraud is a deceptive malicious strategy that relies on repetitive mimicking of human clicking on online advertisements, without actual intention to complete a purchase. This fraud can result in significant financial loses for both advertising companies and marketers, and at the same time destroying their public images. Nevertheless, detection of these illegitimate clicks is very challenging as they closely resemble to authentic human engagement. This study examines the utilization of artificial intelligence approaches to detect deceptive clicks, by identifying subtle correlations between the timing of the clicks, taking into account their geographical or network sources and linked application sources as indicators to separate legitimate from malicious activity. This study highlights the application of recurrent neural networks (RNNs) for this task, keeping in mind that the process of selection and tuning of the model's hyperparameters plays a vital role in the performance. An adapted implementation of crayfish optimization algorithm was consequently proposed in this paper, and used to optimize RNN models to enhance their general performance. The developed framework was evaluated utilizing actual operational datasets and yielded encouraging outcomes. article 10.56578/jii020404 2 4 230 238 2958-2687 Journal of Industrial Intelligence