Received: 17 April 2016 Revised: 12 December 2016 Accepted: 7 March 2017
SPECIAL ISSUE PAPER
Protecting Internet users from becoming victimized attackers
Md Shahrear Iqbal
School of Computing, Queen's University,
Kingston, Ontario, Canada
Ubitrak, Saint-Laurent, Quebec, Canada
Irdeto, Ottawa, Ontario, Canada
Md Shahrear Iqbal, School of Computing,
Queen's University, Room 536, Kingston,
Mitacs Canada; Irdeto Canada
Internet users are often victimized by malicious attackers. Some attackers infect and use innocent
users' machines to launch large-scale attacks without the users' knowledge. One of such attacks
is the click-fraud attack. Click-fraud happens in pay-per-click ad networks where the ad net-
work charges advertisers for every click on their ads. Click-fraud has been proved to be a serious
problem for the online advertisementindustry. In a click-fraud attack, a user or an automated soft-
ware clicks on an ad with a malicious intent and advertisers need to pay for those valueless clicks.
Among many forms of click-fraud, botnets with the automated clickers are the most severe ones.
In this study, we present a method for detecting automated clickers from the user side. The pro-
posed method to fight click-fraud, FCFraud, can be integrated into the desktop and smart device
operating systems. Since most modern operating systems already provide some kind of antimal-
ware service, our proposed method can be implemented as a part of the service. We believe that
an effective protection at the operating system level can save billions of dollars of the advertisers.
Experiments show that FCFraud is 99.6% (98.2% in mobile ad library–generated traffic) accurate
in classifying ad requests from all user processes and it is 100% successful in detecting clickbots
in both desktop and mobile devices. We implement a cloud backend for the FCFraud service to
save battery power in mobile devices. The overhead of executing FCFraud is also analyzed and we
show that it is reasonable for both the platforms.
click-fraud, malware detection, online advertising
Online advertising is a form of marketing, which uses digital medium
(eg, websites and mobile apps) to deliver promotional messages to con-
sumers. It includes email marketing, search engine marketing (SEM),
social media marketing, and many types of display advertising. It is the
main financial incentive for free Web contents and services as well
as free mobile apps. The online advertising industry is a billion dollar
industry with ad spending projected to reach $161 billion in desktop
and $101 billion in mobile devices by 2016.
The largest revenue
shares within internet advertising are generated by display-based and
Advertisers and Web/mobile publishers use a wide range of rev-
enue models. Among all the models, pay per click (PPC) is the dominant
one. In PPC, advertisers pay each time a user clicks on an ad. The
biggest threat to the PPC advertisement is click-fraud.
the practice of deceptively clicking on online ads with the intention of
either increasing website/mobile app revenues or exhausting an adver-
tiser's budget. Click-fraud is a major concern for advertisers, and many
researchers proposed methods to detect and block suspicious clicks on
Among all forms of click-fraud, attacks by the botnets are the
most severe. Attackers use botnets extensively in recent years to
launch large-scale attacks
like click-fraud. A botnet is a network of
malware-infected devices that are controlled by a botmaster. The users
are normally unaware of the fact that their devices are compromised
and used by the attackers. Fight Click-Fraud (FCFraud) particularly pro-
tects this group of users from being exploited.
To combat click-fraud, ad networks mostly use server-side tech-
niques. They gather information from different sources about users.
Then, they apply machine learning or pattern recognition techniques to
identify suspicious clicks. Despite all these efforts, click-fraud remains
as a primary problem for the online advertising industry because of
the lack of control over the client machines from the server side.
J Softw Evol Proc. 2018;30:e1871.
wileyonlinelibrary.com/journal/smr Copyright © 2017 John Wiley & Sons, Ltd. 1of15