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BizReport : Advertising archives : March 05, 2019

Study finds more traffic fraudulent

Fraudulent traffic has been a problem for more than a decade, but one new report indicates the problem may be even larger than most brands think. According to new data out from CHEQ up to 77% of US traffic is fraudulent; not only that, but this traffic is coming from sophisticated sources which can make it hard to detect.

by Kristina Knight

CHEQ is an AI-based cybersecurity hub; their data found that three-quarters (77%) of US web traffic is SIVT - or sophisticated invalid traffic. SIVTs use more sophisticated methods of traffic than typical fraudulent traffic - sometimes called GIVT or generalized invalid traffic. What this means is that while GIVT can be identified relatively simply used IP and blacklists, SIVT needs things like device fingerprinting, honeypots and highly skilled OS's to be detected.

"While many U.S. advertisers believe that they are being exposed to fairly basic and rudimentary attacks, most of the fraud today is highly sophisticated. First-generation ad verification is ineffective in dealing with the growing scale of online ad fraud, including clickjacking, domain and device spoofing and zombie networks. With our use of more than 700 parameters in real time across every single ad request, we're uncovering far more sophisticated types of fraud as cybercriminals constantly up their game," said Guy Tytunovich, CHEQ Founder.

CHEQs system found 570 million SIVT attacks; these attacks used fraudulent methods including domain-masking, invalid referrals, and view ability fraud that were all woven together throughout the attacks.

Other interesting findings from the CHEQ research:

▪ 55% of online fraud in the US is desktop based, 46% is mobile
▪ Of mobile fraud, 49% is Android-account based, 41% is Apple-account based
▪ 49% of browser based ad fraud comes from Chrome, 31% from Safari

More CHEQ data can be accessed here.

Tags: ad fraud, ad fraud trends, advertising, ai security, CHEQ, fraudulent traffic, GIVT fraud, SIVT fraud

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