Author: Steffen Meyer, Mobile Marketing Content Specialist
Ad fraud is rising, with an estimated 100 billion euros expected to be pilfered from digital marketing budgets in 2023.
In the world of digital marketing, it’s akin to the Wild West. To avoid becoming a victim of this robbery, investing in your own private security is paramount. Enter the Mobile Measurement Partner, or MMP (Read here on how to customize your MMP and how to coordinate your team when implementing it). This tool comes equipped with built-in filters designed to thwart ad fraud, saving both time and money while ensuring that your statistics remain untainted by criminal activities.
In previous blog posts, we’ve delved into the basics and advanced tactics of fraud, as well as the emergence of AI-driven content farms. This time, we will focus on how an MMP, such as Adjust, identifies and thwarts these tactics, preventing digital malefactors from siphoning your budget.
This protection is accomplished through a multi-layered approach:
Layer 1: Distribution Modeling to Counter Click Spam
To falsely claim an attribution, fraudsters simulate a barrage of clicks, for example, by running a fraudulent app in the background.
Adjust identifies these repetitive engagements originating from the same source and detects anomalies in the distribution of click-to-install times, which deviate from standard user behavior.
When one of these filters exposes fraudulent activity, the MMP attributes the install to the next most relevant engagement source or, when that’s not feasible, categorizes it as organic.
Layer 2: Timestamp Filtering to Combat Click Injection
Click Spam’s more sophisticated cousin is Click Injection. Fraudsters exploit Android’s „broadcast intents“ by sending out clicks just moments before an app is installed to fraudulently collect rewards.
To detect this behavior, Adjust retrieves the install start and finish times through the Google Referrer API and the SDK initialization. By including these timestamps, the MMP accurately determines whether an incoming engagement genuinely leads a user to open and interact with an app for the first time.
As with Click Spam, Adjust either identifies the next most suitable source for attribution or registers the install as organic.
Layer 3: Anonymous IP Detection to Thwart Fake Installs
Fake Installs involve fraudsters mimicking real installs through manual humans performing the install, automated scripts with connected devices, or virtual environments where fake installation processes are entirely automated.
The Anonymous IP Detection filter cross-references the IP address of each install with the MaxMind database, which categorizes IP addresses globally and flags known data centers and anonymizing services.
If the IP address is associated with a hosting service, VPNs, or TOR, the filter rejects the attribution and logs the install as originating from an untrusted device, indicating that the user isn’t genuine.
Layer 4: SDK Signature to Defend Against SDK Spoofing
Fraudsters, through trial and error, identify communication signals between tracking software, the SDK, and the backend server corresponding to first opens or installs. They then send these signals to claim downloads and advertising revenue.
To ensure that the signal genuinely originates from the SDK and not from a „Man-in-the-middle“ attacker, Adjust has developed the SDK Signature. Each client receives a unique signature, and whenever the Adjust backend is contacted, the MMP verifies whether it matches their database.
A mismatch prompts Adjust to reject the attribution and classify the install as coming from an untrusted device.
Employing an MMP is Crucial in the Fight Against Ad Fraud
The use of an MMP is paramount in the battle against ad fraud. Taking ad fraud lightly could lead to unknowingly falling prey to scammers and basing your marketing strategies on inaccurate statistics, potentially resulting in waisting thousands of euros or dollars.
If you’re uncertain about your next steps, our consultants are here to assist you. Reach out for expert guidance today.
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