ADREMOVER: THE IMPROVED MACHINE LEARNING APPROACH FOR BLOCKING ADS There is an explosion in the advertisements over web nowadays. Most of the websites we visit contain ads, even Facebook, Google and Twitter. Sometimes, it could also appear that someone is spying on us because there are incidents like ads that show up with the content exactly what you have been searching not long ago. Such events happen as a result of Web Tracking. Initially, ads were meant to support businesses and companies to market their products and persuade the users to purchase them. Web Tracker were meant to track the user interaction with the website so it can improve the user experience. However, some of these have allowed ads as malvertisements, which may take advantage of these functionalities to steal the users sensitive information. To counter this tsunami of ads (malware), several ad blockers were created and are freely available as a browser extension and serve the purpose of blocking ads and trackers and most of them use the handcrafted filter lists, some of them apply the machine learning approach. However, because of outdated filterlist or whitelist and also inability to identify brand new ad signatures, most of them do not provide the depth of functionality and intelligence required to block all the undesirable content. After applying several machine learning approaches and comparing them, we propose our tool, AdRemover, that is a novel approach using the list of URLs which contains Ad and NonAd as the dataset. The classification applying Random Forest exceeds 98% with the splitted 20% testing data and 80% training data from our dataset.

Targeted display advertising: the case of preferential attachment An average adult is exposed to hundreds of digital advertisements daily (this https URL), making the digital advertisement industry a classic example of a bigdatadriven platform. As such, the adtech industry relies on historical engagement logs (clicks or purchases) to identify potentially interested users for the advertisement campaign of a partner (a seller who wants to target users for its products). The number of advertisements that are shown for a partner, and hence the historical campaign data available for a partner depends upon the budget constraints of the partner. Thus, enough data can be collected for the highbudget partners to make accurate predictions, while this is not the case with the lowbudget partners. This skewed distribution of the data leads to preferential attachment of the targeted display advertising platforms towards the highbudget partners. In this paper, we develop domainadaptation approaches to address the challenge of predicting interested users for the partners with insufficient data, i.e., the tail partners. Specifically, we develop simple yet effective approaches that leverage the similarity among the partners to transfer information from the partners with sufficient data to coldstart partners, i.e., partners without any campaign data. Our approaches readily adapt to the new campaign data by incremental finetuning, and hence work at varying points of a campaign, and not just the coldstart. We present an experimental analysis on the historical logs of a major display advertising platform (this https URL). Specifically, we evaluate our approaches across 149 partners, at varying points of their campaigns. Experimental results show that the proposed approaches outperform the other domainadaptation approaches at different time points of the campaigns.