@article{minastireanu2019light,
  title = {Light GBM Machine Learning Algorithm  to Online Click Fraud Detection},
  author = {    Elena-Adriana MINASTIREANU and Gabriela MESNITA},
  year = 2019,
  url = {https://ibimapublishing.com/articles/JIACS/2019/263928/},
  journal = {Journal of Information Assurance & Cybersecurity},
  volume = 2019,
  pages = 12,
  doi = DOI: 10.5171/2019.263928,
  abstract = {In the current web advertising activities, the fraud increases the number of risks for online marketing, advertising industry and e-business. The click fraud is considered one of the most critical issues in online advertising. Even if the online advertisers make permanent efforts to improve the traffic filtering techniques, they are still looking for the best protection methods to detect click frauds. Hence, an effective fraud detection algorithm is essential for online advertising businesses. The purpose of our paper is to identify the precision of one of the modern machine learning algorithms in order to detect the click fraud in online environment. In our research, we have studied click patterns over a dataset that handles 200 million clicks over four days. The main goal was to assess the journey of a user’s click across their portfolio and flag IP addresses who produce lots of clicks, but never end up in installing apps. As a methodology, we use the experimental test for LightGBM - a Gradient Boosting Decision Tree-type method. This algorithm has enabled an accuracy of 98%. In our research, the literature review was the central source to verify our results.},
  keywords = {Keywords: online click fraud, machine learning, algorithm classifications, gradient methods},
  note = Article ID: 263928
}