An Ensemble Learning Approach to Detect Touch-Spam in Mobile Ad-Networks
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International Journal of P2P Network Trends and Technology (IJPTT) |  |
| © 2017 by IJPTT Journal | ||
| Volume - 7 Issue - 2 |
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| Year of Publication : 2017 | ||
| Authors : M.Sree Vani |
Citation
M.Sree Vani "An Ensemble Learning Approach to Detect Touch-Spam in Mobile Ad-Networks". International Journal of P2P Network Trends and Technology (IJPTT), V7(2):18-24 Mar - Apr 2017, ISSN:2249-2615, www.ijpttjournal.org, Published by Seventh Sense Research Group.
Abstract
A touch user interface (TUI) is a computer-pointing technology based upon the sense of touch (haptics). Touch-spam is a type of fraud that occurs over TUI gadgets ex. Smart phones, tablets, phablets, touch laptops etc. It actually happens in TUI applications when a person, automated script, computer program or robotic action imitates a legitimate user of a TUI application touching on an advertisement (ad), for the purpose of generating a charge per touch without having actual interest in the target of the ad’s popup. Touch-spam is becoming an issue due to the advertising networks being a key beneficiary of this spam. In present days, smart phone gaming applications (apps) are playing a vital role to attract mobile-advertisements (ads) since their pocket portability and other versatile features. Popular apps are able to read the user personalized data to process user interests helping to generate customized ads. Touch-spam in smart phone apps is a fraudulent or invalid tap or touch on online ads, where the user has no actual interest in the advertiser’s site. It requires a user touch on online ads that pop-up dynamically in smart phone gaming apps. It all need the user to tap the screen close to where the ad is displayed .While the ad networks continue taking active measures to block click-spam today, the touch-spam still creeping under the TUI. It is being used by spammers to misappropriate the advertising revenue. The presence of touch-spam is largely unknown. In this paper, we take the first systematic look at touch-spam. We propose an ensemble learning approach to identify touch-spam Apps in Smartphone-game Apps. We validate our methodology using data from major ad networks. Our findings highlight the severity of the touch-spam problem.
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Keywords
Spam, mobile apps, touch spam, click spam