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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 – TWICE that of 2015
- 3. Dangers From theFront Line Sources: Magna Global Hewlett Packard Enterprises, “The Business of Hacking”, May 2016 Wow, that was easy!
- 4. DSP Who Lives InThis New Frontier ?
- 5. Who Is atRisk ? Everyone • Developers & Publishers • Networks, DSPs, SSPs, Exchanges • Attribution companies & Solution Providers • And of course …. Agencies, Marketers & Advertisers Threats • $$$ • Resource drain • Misdirected focus • Business model alignment • Misaligned user experience • Reputation
- 6. Mobile Ad FraudHotspots
- 7. Who Is toBlame ? The rest • The App Stores • The Operating System • Publishers/Developers • Ad Networks/SSPs/Exchanges • Attribution companies/Solution Providers • Advertisers/DSPs The bad guys • Intentional criminals
- 8. Types of Fraud Areasto consider: • Impression // CPM • Click // CPC • Install // CPI • Lead // CPL • Injection // Adware • Domain Spoofing • CMS // Fake Publisher • Blending // Audience Extension
- 9. Attack Vectors Non-Human Traffic: •Simple Bots • Complex Bots • Botnets Human Traffic: • Invisible Ads • Domain Spoofing • Click Spam • Click Injection • Click Farms
- 10. Non-Human Traffic -Bots Type • Simple Bots – simple scripts that run from hosting servers with consistent patterns. • Complex Bots – sophisticated tactics mimicking normal behavior. • Botnets – array of devices that have been compromised by bad actors. Send commands that perform tasks like ‘loading’ or ‘clicking’ on ads or ‘installing’ and ‘opening’ other apps Detection • Patterns can be identified then blocked eg. IDs; agents; known data center IP addresses. • More difficult as less consistent pattern. Rotating IP; user agents; ids; timings; ctr. • Hard to detect and block. When discovered by law enforcement effectively shutdown. Patterns can be uncovered by experts.
- 11. Type • Invisible ads– hidden ads with zero being seen aka Ad stacking Detection • Very low CTR/high CTR with other characteristics. Detectable using off the shelf ad verification tools like Integral Ad Science. Human Traffic – Invisible Ads
- 12. Type • Domain Spoofing– publishers are declare their own domain and label They misrepresent by identifying as domain. Other cases the publisher spoofed within the request. Detection • Digging deeper and doing proper verification and validation of publishers. Domain Spoofing
- 13. Type • Click Spam– clicks, clicks and generated in hope of “winning” or install. Detection • Low/fixed conversion rate • High amount of clicks • 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 • Click Injection– a process on the to “broadcast intents” for app a click before the app is opened in the install due to last click Detection • Android only • Conversion rates are 100% • Low CTIT/MTTI • High concentration of installs within moments of click Click Injection
- 16. CLICK TRACK DOWNLOADOPEN NEW CLICK ! Last Click Wins Attribution based on “open” event Click Injection
- 17. Example of ClickInjection Installs Extremely short install times (within seconds) indicate an injection pattern Install Time Analysis Seconds
- 18. Type • Click Farms- a large group of low- are hired to click on paid Detection • Very difficult as visitors are real • Repeated patterns Click Farms
- 24. Example of HowIt’s Done
- 25. Prevent Mobile AdFraud In The First Place Detection: Look for Deviations from Patterns / Baselines • Establish a baseline; Compare activity against it. • Flag/report any abnormalities, discrepancies asap Monitor and measure everything Notice Installs from Suspicious Sources Assign an Internal Stakeholder • Dedicated internal person to look at performance
- 26. Prevention: Buy Direct WhenPossible • Direct relationships improve quality and transparency Buy Premium • Cheap traffic comes at a high price Partner Up • Ad fraud security services • Measurement + attribution partners Research • Reputations matter Protect Yourself • Contract that outlines what you will/will not accept Prevent Mobile Ad Fraud
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