Publication

Abstract : The use of web-based services has been soaring since the internet has come up. Hence, traditional forms of advertisements have moved on to the web, leading to a rising number of ad services and trackers affecting user privacy. Ad blockers were developed in order to prevent these. However, the currently available ad blockers are purely based on a predefined set of blacklist domains. This study proposes a machine learning-based approach to detect web-based ad services and trackers. In order to help us explore the area using machine learning techniques, a dataset was created. The developed dataset contained 74,000 entries comprising both domains that served advertisements and normal domains. In addition, we have also compared the scores that were obtained by various supervised machine learning models, and the best model was identified. The best model offered up to 88% accuracy in classifying ad services and normal websites. The model contributed by this paper could be further developed into a machine learning-based advertisement blocker system.