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Mobile Ad Fraud Wikileaks: Exposing the Threats
The document discusses the rising issue of mobile ad fraud, highlighting its impact on various stakeholders in the mobile advertising ecosystem as the industry grows to $143 billion. It covers different types of fraud, detection methods, and strategies for prevention, emphasizing the complexity and sophistication of fraudulent activities. Furthermore, the document suggests forming direct relationships and leveraging ad fraud security services to combat these threats effectively.
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Mobile Ad Fraud Wikileaks: Exposing the Threats
- 1. UN//CLASSIFIED (TOPIC) MOBILE ADFRAUD WIKILEAKS EXPOSING THE THREATS Rev 1.1, 31 July 2017 Presenter: Dale Carr Email: dale.carr@Leadbolt.com Position: CEO, LeadboltUN//CLASSIFIED
- 2. The Mobile Frontier Worldwide,$143 billion will be spent on mobile ads this year โ TWICE that of 2015
- 3. Dangers From theFront Line Sources: Magna Global Hewlett Packard Enterprises, โThe Business of Hackingโ, May 2016 Wow, that was easy!
- 4. DSP Who Lives InThis New Frontier ?
- 5. Who Is atRisk ? Everyone โข Developers & Publishers โข Networks, DSPs, SSPs, Exchanges โข Attribution companies & Solution Providers โข And of course โฆ. Agencies, Marketers & Advertisers Threats โข $$$ โข Resource drain โข Misdirected focus โข Business model alignment โข Misaligned user experience โข Reputation
- 6. Mobile Ad FraudHotspots
- 7. Who Is toBlame ? The rest โข The App Stores โข The Operating System โข Publishers/Developers โข Ad Networks/SSPs/Exchanges โข Attribution companies/Solution Providers โข Advertisers/DSPs The bad guys โข Intentional criminals
- 8. Types of Fraud Areasto consider: โข Impression // CPM โข Click // CPC โข Install // CPI โข Lead // CPL โข Injection // Adware โข Domain Spoofing โข CMS // Fake Publisher โข Blending // Audience Extension
- 9. Attack Vectors Non-Human Traffic: โขSimple Bots โข Complex Bots โข Botnets Human Traffic: โข Invisible Ads โข Domain Spoofing โข Click Spam โข Click Injection โข Click Farms
- 10. Non-Human Traffic -Bots Type โข Simple Bots โ simple scripts that run from hosting servers with consistent patterns. โข Complex Bots โ sophisticated tactics mimicking normal behavior. โข Botnets โ array of devices that have been compromised by bad actors. Send commands that perform tasks like โloadingโ or โclickingโ on ads or โinstallingโ and โopeningโ other apps Detection โข Patterns can be identified then blocked eg. IDs; agents; known data center IP addresses. โข More difficult as less consistent pattern. Rotating IP; user agents; ids; timings; ctr. โข Hard to detect and block. When discovered by law enforcement effectively shutdown. Patterns can be uncovered by experts.
- 11. Type โข Invisible adsโ hidden ads with zero being seen aka Ad stacking Detection โข Very low CTR/high CTR with other characteristics. Detectable using off the shelf ad verification tools like Integral Ad Science. Human Traffic โ Invisible Ads
- 12. Type โข Domain Spoofingโ publishers are declare their own domain and label They misrepresent by identifying as domain. Other cases the publisher spoofed within the request. Detection โข Digging deeper and doing proper verification and validation of publishers. Domain Spoofing
- 13. Type โข Click Spamโ clicks, clicks and generated in hope of โwinningโ or install. Detection โข Low/fixed conversion rate โข High amount of clicks โข Patterns in click frequency Click Spam
- 14. Example of ClickSpam Installs Even distribution (flat lines) over a number of hours is an indication of spamming activity Early Installs show a normal pattern 0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 Hours Install Time Analysis
- 15. Type โข Click Injectionโ a process on the to โbroadcast intentsโ for app a click before the app is opened in the install due to last click Detection โข Android only โข Conversion rates are 100% โข Low CTIT/MTTI โข High concentration of installs within moments of click Click Injection
- 16. CLICK TRACK DOWNLOADOPEN NEW CLICK ! Last Click Wins Attribution based on โopenโ event Click Injection
- 17. Example of ClickInjection Installs Extremely short install times (within seconds) indicate an injection pattern Install Time Analysis Seconds
- 18. Type โข Click Farms- a large group of low- are hired to click on paid Detection โข Very difficult as visitors are real โข Repeated patterns Click Farms
- 24. Example of HowItโs Done
- 25. Prevent Mobile AdFraud In The First Place Detection: Look for Deviations from Patterns / Baselines โข Establish a baseline; Compare activity against it. โข Flag/report any abnormalities, discrepancies asap Monitor and measure everything Notice Installs from Suspicious Sources Assign an Internal Stakeholder โข Dedicated internal person to look at performance
- 26. Prevention: Buy Direct WhenPossible โข Direct relationships improve quality and transparency Buy Premium โข Cheap traffic comes at a high price Partner Up โข Ad fraud security services โข Measurement + attribution partners Research โข Reputations matter Protect Yourself โข Contract that outlines what you will/will not accept Prevent Mobile Ad Fraud
- 27.