Of course, this is a simplified image of the ML algorithms in work.
Another good example of the fraudster’s fast reactions is the “% of new device” metrics. This metric could be very efficient in revealing some type of fraud. But the problem still remains the same. Fraudsters know this metric.
So within some time the app is flooded again with the unexplained increase in number of downloads without any further activity inside the product. One of the hypotheses which should be named is that all these devices aren’t more ‘new’, and the rule doesn’t work here anymore.
Let’s mention here ‘carousel effect’ too. When the fraudsters have modified and improved their algorithm or methods, they will try to ‘infect’ you again and again within the time. Very often they do that using other ‘sub-IDs’, while working with your biggest publishers, whom you scale consistently.
By aiming the steady growth, each developer wants to see increases pretty consistently across the markets. The same is about fraudsters. Among 100 installs bought at least 25 of them are fraudulent.
A commonly used approach of traditional anti-fraud solutions in detecting the app-install ad fraud is the rules-based analysis, as was mentioned before. Even with using buzzwords as “machine learning”, “big data”, “artificial intelligence” that are still rules, heuristics inside.
All fraud reasons and their specific meanings are available to clients (existing traditional anti-fraud solutions) via website / via dashboard / via numerous white-paper and presentations / via account managers (anti-fraud solutions).
They are also available to publishers and sub-publishers via the same channels, plus they receive the detailed rejections reports from user acquisition managers. Thus, the fraudsters can easily see it and use to reverse-engineer and re-work their strategy. That is why, in the majority of cases they new “attacks” succeed.
Unfortunately, the traditional rules-based analysis just isn’t efficient in detecting the new modified fraud patterns.
And, of course, the developer is unable to make a clear decision on this traffic too, as the pattern is already different, more advanced. Of course, in this situation UA team proceeds paying for the fraudulent traffic and very often scale these sources.
Only deep and machine learning technology backed solution can solve the problem. Machine learning analyses the dozens of features and a hundred of connections between these features, which are not seen or understandable to humans. That makes it flexible and capable to detect absolutely new types and patterns of fraud.
This methodology significantly increases the level of protection.