Specialised solutions help with ad scam protection, but not everyone employs the same methods. Here are some of the most popular ad fraud protection techniques, which is click fraud protection.
Methods that are physical or semi-hand
IP blocking, also known as eliminating particular IP addresses and servers from accessing websites and thus ads. It can get accomplished manually, and some solutions keep their own eliminates to block all clicks and installations from banned sources. This way of prevention, however, is extremely limited in its ability to stop fraudsters’ methods. And ineffective at preventing ad fraud or accurately analysing traffic to mitigate wrong alarms.
Prevention founded on rules
Ad fraud trends get identified using preset heuristic rules in rules-based anti-fraud protection systems. If traffic or user behaviour exceeds the parameters’ normal range specified by expert-created guidelines, it will be marked and stopped.
Statistical investigation
Some ad fraud solutions use statistical analysis to determine how probable user behaviour is fake. It entails using distribution estimates and mean variation to determine whether traffic is “normal” or includes odd patterns.
To identify ad fraud, statistical analysis devices frequently examine the following variables:
- The amount of travel
- Rates of conversion
- Fingerprint distribution
However, numerous industry-specific statistics can be considered when developing systems that perform statistical analysis, such as time between transactions, daily distribution, or deviation between various traffic sources, to name a few.
Techniques
These are some of the techniques used for protecting ad fraud.
Honeypot-based
The honeypot-based method detects ad theft and bot activity by inserting custom script inserts into advertising forms. The “honeypot” is an extra field the viewer cannot see is the best click fraud protection. Bots are ignorant of the bluff field and fill it in regardless, triggering a denial process to avoid fake efforts.
A method based on anomalies
The anomaly-based method analyses traffic for unusual occurrences and anomalies as traffic rises and sudden surges in views.
Signature-based
Signature-based solutions identify suspect behaviour (traffic, views, and impressions) by comparing trends to observed activity to determine legitimacy.
Prevention and identification of behavioural issues
Based on the mechanism’s knowledge of human-like behaviours, behavioural detection and prevention techniques anticipate whether user behaviour is genuine. As a result, it’s an effective instrument for identifying poor bot activity.
This technique uses its understanding of how users typically act to determine whether the user is legitimate, stopping ad fraud before it destroys campaigns. Among the typical user actions studied by this detection mechanism are:
- Scrolling habits
- Keystrokes
- Non-linear/randomised/patterned mouse dynamics and motions
- Mouse movements (identifying rhythms)
- The number of inquiries made during a period.
Learning by machine
Machine learning is the most effective method for combating fraud because it avoids statistical and rule-based detections caused by large quantities or highly complex bots. Machine learning uncovers patterns and finds connections within databases, and it uses previous data to categorise new data to forecast ad fraud. Ad fraud methods can easily conceal volumes of data, but machine learning can detect patterns by quickly analysing multiple variables. As more data gets fed into the engine, machine learning algorithms begin to self-train to identify various trends with little human involvement.
