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Paper Title : MOBILE ADVERTISEMENT FRAUD DETECTION USING MACHINE LEARNING

ISSN : 2395-1303
Year of Publication : 2021
10.29126/23951303/IJET-V7I4P19

Authors: MR.S.Kaviarasan,Nellore Sai Gokul,Chintagumpala Jaswanth,Madithati Nikhil Reddy

         


Abstract
- With ongoing advancements in the field of technology, mobile advertising has emerged as a platform for publishers to earn profit from their free applications. An online attack commonly known as click fraud or ad fraud has added up to the issue of concerns surfacing mobile advertising. Click fraud is the act of generating illegitimate clicks or data events in order to earn illegal income. Generally, click frauds are generated by infusing the genuine code with some illegitimate bot, which clicks on the ad acting as a potential customer. This social network analysis model takes into consideration a wide range of parameters from a large group of users around the world. In this work, we are going to study & analysing about the fraud detection. In this work, we are going to analyse the performance of machine learning classification methods, and classify as “is attributed” which an application can reach. It allows advertisers to take their product out to new audiences and app developers to escalate their application reach to new markets. Another common name for mobile advertising is inapp advertising. This in-app advertising comprises of four major components, namely: 1) The advertiser, 2) The user, 3) The publisher, and 4) The ad network. A user, in mobile advertising is one who views the ad. The owner of the product which is being advertised is considered to be the advertiser whereas the person to whom the application, in which the advertisement is or “not attributed”. A machine learning model like XGBoost, Lightlgm, multiple encoding methods are applied for the prediction process. The complete implementation can be done through Google Colab (Python-Jupyter Notebook).

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