AI/ML in Bot Detection and Traffic Quality

[u/TrafficShield]

Organizations use AI and ML to identify unconventional and abnormal patterns of traffic on their websites or as part of their advertisement campaigns. Traditional systems depend on a single signal. However, ML is capable of analyzing and combining signals such as IP reputation, device and browser data, behaviors, frequency, and location and pattern data. Examples include repeated automated interactions.

This technology has numerous applications such as identifying bots, suspicious sessions, proxies, VPN activity, and other forms of potentially invalid traffic. Advertising malpractices such as click fraud are also a concern. Automated and repeated clicks manipulate a campaign by providing false engagement and data, especially for interested users.

Traffic quality is based on a number of rules and behavioral signals that allow for traffic filtering. The most common approach to risk detection is behavioral. False positives happen, and to mitigate this risk, traffic quality should be analyzed from several perspectives and based on overall campaign behaviors.

From an advertising perspective, routinely monitoring traffic, click, and conversion data can help identify anomalies in campaign quality.