Ad Click Fraud Detection Using Machine Learning and Deep Learning Algorithms
In online advertising, click fraud poses a significant challenge, draining budgets and threatening the industry’s integrity by redirecting funds away from legitimate advertisers. Despite ongoing efforts to combat these fraudulent practices, recent data emphasizes their widespread and pers...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10847816/ |
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| author | Reem A. Alzahrani Malak Aljabri Rami A. Mustafa Mohammad |
| author_facet | Reem A. Alzahrani Malak Aljabri Rami A. Mustafa Mohammad |
| author_sort | Reem A. Alzahrani |
| collection | DOAJ |
| description | In online advertising, click fraud poses a significant challenge, draining budgets and threatening the industry’s integrity by redirecting funds away from legitimate advertisers. Despite ongoing efforts to combat these fraudulent practices, recent data emphasizes their widespread and persistent nature. Toward detecting click fraud effectively, this study employed a comprehensive feature engineering and extraction approach to identify subtle differences in click behavior that could be used to distinguish fraudulent from legitimate clicks. Subsequently, a thorough evaluation was conducted involving nine diverse machine learning (ML) and Deep Learning (DL) models. After Recursive Feature Elimination (RFE), the ML models consistently demonstrated robust performance. DT and RF surpassed 98.99% accuracy, while GB, LightGBM, and XGBoost achieved 98.90% or higher. Precision scores, measuring accurate identification of fraudulent clicks, exceeded 98% for models like ANN. In parallel, deep learning (DL) models, including Convolutional Neural Network (CNN), Deep Neural Network (DNN), and Recurrent Neural Network (RNN), showcased strong performance. RNN, in particular, achieved 97.34% accuracy, emphasizing its efficacy. The study underscores the prowess of tree-based methods and advanced algorithms in detecting click fraud, as evidenced by high accuracy, precision, and recall scores. These findings contribute valuable insights to combat click fraud and establish the groundwork for the strategic development of anti-fraud measures in online advertising. |
| format | Article |
| id | doaj-art-5f3c124c0fc74f0da6cefacafeb686cf |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-5f3c124c0fc74f0da6cefacafeb686cf2025-01-28T00:01:48ZengIEEEIEEE Access2169-35362025-01-0113127461276310.1109/ACCESS.2025.353220010847816Ad Click Fraud Detection Using Machine Learning and Deep Learning AlgorithmsReem A. Alzahrani0https://orcid.org/0009-0008-5098-4083Malak Aljabri1https://orcid.org/0000-0002-9255-6094Rami A. Mustafa Mohammad2https://orcid.org/0000-0002-2612-1615Department of Computer Science, College of Computer Science and Information Technology, SAUDI ARAMCO Cybersecurity Chair, Imam Abdulrahman Bin Faisal University, Dammam, Saudi ArabiaDepartment of Computer and Network Engineering, College of Computing, Umm Al-Qura University, Makkah, Saudi ArabiaDepartment of Computer Information Systems, College of Computer Science and Information Technology, SAUDI ARAMCO Cybersecurity Chair, Imam Abdulrahman Bin Faisal University, Dammam, Saudi ArabiaIn online advertising, click fraud poses a significant challenge, draining budgets and threatening the industry’s integrity by redirecting funds away from legitimate advertisers. Despite ongoing efforts to combat these fraudulent practices, recent data emphasizes their widespread and persistent nature. Toward detecting click fraud effectively, this study employed a comprehensive feature engineering and extraction approach to identify subtle differences in click behavior that could be used to distinguish fraudulent from legitimate clicks. Subsequently, a thorough evaluation was conducted involving nine diverse machine learning (ML) and Deep Learning (DL) models. After Recursive Feature Elimination (RFE), the ML models consistently demonstrated robust performance. DT and RF surpassed 98.99% accuracy, while GB, LightGBM, and XGBoost achieved 98.90% or higher. Precision scores, measuring accurate identification of fraudulent clicks, exceeded 98% for models like ANN. In parallel, deep learning (DL) models, including Convolutional Neural Network (CNN), Deep Neural Network (DNN), and Recurrent Neural Network (RNN), showcased strong performance. RNN, in particular, achieved 97.34% accuracy, emphasizing its efficacy. The study underscores the prowess of tree-based methods and advanced algorithms in detecting click fraud, as evidenced by high accuracy, precision, and recall scores. These findings contribute valuable insights to combat click fraud and establish the groundwork for the strategic development of anti-fraud measures in online advertising.https://ieeexplore.ieee.org/document/10847816/Click fraudmachine learningdeep learningonline-advertisingbot detectionpay-per-click |
| spellingShingle | Reem A. Alzahrani Malak Aljabri Rami A. Mustafa Mohammad Ad Click Fraud Detection Using Machine Learning and Deep Learning Algorithms IEEE Access Click fraud machine learning deep learning online-advertising bot detection pay-per-click |
| title | Ad Click Fraud Detection Using Machine Learning and Deep Learning Algorithms |
| title_full | Ad Click Fraud Detection Using Machine Learning and Deep Learning Algorithms |
| title_fullStr | Ad Click Fraud Detection Using Machine Learning and Deep Learning Algorithms |
| title_full_unstemmed | Ad Click Fraud Detection Using Machine Learning and Deep Learning Algorithms |
| title_short | Ad Click Fraud Detection Using Machine Learning and Deep Learning Algorithms |
| title_sort | ad click fraud detection using machine learning and deep learning algorithms |
| topic | Click fraud machine learning deep learning online-advertising bot detection pay-per-click |
| url | https://ieeexplore.ieee.org/document/10847816/ |
| work_keys_str_mv | AT reemaalzahrani adclickfrauddetectionusingmachinelearninganddeeplearningalgorithms AT malakaljabri adclickfrauddetectionusingmachinelearninganddeeplearningalgorithms AT ramiamustafamohammad adclickfrauddetectionusingmachinelearninganddeeplearningalgorithms |