(CBN - Global) May 13 -- Do deceptive ads account for 21 percent of global mobile Internet advertising traffic? They do according to data from a sample survey on the global mobile Web carried out last month by Beijing-based Cheetah Mobile Inc., the world's leading mobile security and utility apps developer.
Where do most of these deceptive promotions originate? How do they make money? Who are the victims? Cheetah Mobile Security Research Lab's findings show that the main sources are pornographic websites, game portals, download sites and search engines.
Some 28 international porn sites such as Pornhub and Xvideos feature explicit adult content and have massive traffic. Though they do not advertise anything themselves, they have been found to display ads with misleading content. These businesses generate revenue by selling traffic to third-party data service platforms (DSPs), and both they and the porn sites are unable to stringently check advertising content.
When users browse adult, movie or gaming sites, they see deceptive banner ads and fake system warnings with messages like 'Your phone has been infected with a virus' or 'Your Android system needs to be upgraded.' They only need to click on an ad and an unnecessary App, such as a browser or system optimization software, will download.
For each installation, fraudsters pocket a commission of between 30 US cents and USD2 from a targeted company. According to market research firm eMarketer, spending on mobile Internet ads in 2016 will reach USD42 billion globally. Assuming that malicious ad traffic accounts for 21 percent of the global market, legitimate advertisers will lose about USD8 billion to fraudsters.
On the mobile Web, which is accessed mainly from smartphones and tablets, the ratio of deceptive ads fluctuates around 20 percent, with many small- and mid-sized platforms and advertisers becoming victims.
Cheetah Mobile Security Research Lab has discovered that many Chinese companies carrying out international promotions, such as UCWeb Inc. and Baidu Inc., have fallen victims to deceptive ads, especially in the wake of the popularity of overseas promotion by Chinese mobile Internet enterprises. Similar fates have also befallen Internet companies from countries including India and Brazil such as OLX Inc. Deceptive ad traffic that entices users to install Apps is attached to mobile ad platforms and sold to companies carrying out global promotions.
There are two main barriers to combating deceptive ads. The first is technology. According to Cheetah Mobile Security Research Lab, fraudulent advertising is spread using high-tech methods to evade technological monitoring by advertising alliances, such as switching ad material in milliseconds and disseminating the ads across different regions, languages and device models. Without the establishment of a high-tech anti-fraud team, it is difficult to make a real-time effective assessment of these ads. However, in reality, most DSPs lack the technological strength and are unable to establish this kind of anti-fraud team.
The second difficulty is that deceptive ads have already become part of the unspoken rules of the mobile Internet advertising industry. As long as there is traffic, ad networks can get advertising fees. Certain advertisers can take good user data and spin a story to the capital markets, in just the same way that a malicious advertiser can earn profits. In this environment, everyone knows that the problem exists and people choose to take a laissez-faire attitude, causing the situation to become increasingly worse. Only users become the real victims, and they are surrounded by spammed advertisements and unable to defend themselves.
Cheetah Mobile Security Research Lab discovered that deceptive ads belong to the following network alliances -- ExoClick, Reporo, PlugRush, SlimTrade and TrafficFactory -- and they, in turn, have also become victims. Cheetah Mobile has said that to cope with deceptive ads on the mobile Web, strong technical knowledge on server and client security needs to be accumulated, while at the same time deep learning and big data have to be put into actual use.