Full Description
The authors systematically review methods of online digital advertising (ad) fraud and the techniques to prevent and defeat such fraud in this brief. The authors categorize ad fraud into three major categories, including (1) placement fraud, (2) traffic fraud, and (3) action fraud. It summarizes major features of each type of fraud, and also outlines measures and resources to detect each type of fraud. This brief provides a comprehensive guideline to help researchers understand the state-of-the-art in ad fraud detection. It also serves as a technical reference for industry to design new techniques and solutions to win the battle against fraud.
Contents
1 Introduction ... 11.1 Computational Advertising ... 11.2 Display Advertising ... 21.3 Fraud in Digital Advertising ... 31.4 Book Objective ... 5References ... 52 Ad Ecosystems and Key Components ... 92.1 Ad Banner and Viewability ... 112.2 Ad Business Model ... 122.3 Revenue Models ... 142.4 Ad Advertisement Concept Flow... 16References ... 193 Ad Fraud Taxonomy and Prevention Mechanisms ... 213.1 Ad Fraud Taxonomy ... 223.2 Ad Fraud Prevention Mechanism ... 23References ... 244 Ad Fraud Categorization and Detection Methods ... 274.1 Placement Fraud ... 274.1.1 Stuffing or Stacking ... 284.1.2 Fake Sites ... 294.1.3 Domain Spoofing ... 294.1.4 Ad Injection and Malware ... 314.2 Traffic Fraud ... 324.2.1 Impression Fraud ... 324.2.2 Click Fraud ... 334.3 Action Fraud ... 354.3.1 Conversion Fraud ... 364.3.2 Re-targeting fraud ...xii Contents4.3.3 Affiliate Fraud ... 37References ... 385 Ad Fraud Measure and Benchmark ... 435.1 Measures with Ground Truth ... 435.2 Measures without Ground Truth ... 465.3 Real World Datasets ... 46References ... 476 Ad Fraud Detection Tools and Systems ... 496.1 Commercial Ad Fraud Detection Systems ... 496.2 Ad Fraud Detection Systems in Academia ... 51References ... 527 CONCLUSION ... 55Glossary ... 57
