2026-08-08T12:23:54.212Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:2:12128 2026-08-01T21:38:10Z 2

Click Fraud Detection With Recurrent Neural Networks Optimized by an Adapted Version of Variable Neighborhood Search Algorithm 2026 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/2/12128 https://onlinelibrary.wiley.com/doi/10.1111/coin.70283 V. Zeljkovic M. Zivkovic F. Al-Turjman J. Gajic L. Babic A. Djordjevic M. Antonijevic N. Bacanin The revenue generated from online ads has become quite significant, and as with the advancement in any sort of business, this one brings fraudsters with it. However, different forms of fraud can be performed as this work tackles the problems of click fraud in advertisements. In this case, the fraud can be performed by the party that bought the advertisement to boost its revenue, or by other malicious parties that tend to exhaust the resources for the said ad, for example. Due to this and many other scenarios, a robust solution for detecting such cases must be established. However, existing click fraud detection approaches either rely on static rule-based systems or deep learning models with manually tuned hyperparameters, which may result in limited adaptability and suboptimal performance in complex sequential environments. Therefore, there is a need for an adaptive optimization strategy capable of effectively tuning sequential models for improved fraud detection accuracy. This work proposes three different types of recurrent neural networks (RNNs) that are combined with the attention mechanism. Furthermore, in each of the three different experiments, the networks were optimized by strong metaheuristics optimizers, the results of which were compared to establish the strongest one. This was done with the purpose of confirming the improvements to the variable neighborhood search (VNS) algorithm, which was proposed by the authors in this work. The best synthesized RNN model tuned by the suggested modified optimizer attained accuracy of 0.806569, with Matthews correlation coefficient of 0.613209. article 10.1111/coin.70283 42 4: e70283 0824-7935 COMPUTATIONAL INTELLIGENCE