Bots now leverage full-fledged browsers and are programmed to mimic human behavior in the way they traverse a website or application, move the mouse, tap and swipe on mobile devices and generally try to simulate real visitors to evade security systems.
Impact: These bots are generally used to carry out scraping, carding and form spam.
These bots use full-fledged browsers — dedicated or hijacked by malware — for their operation. They can simulate basic human-like interactions, such as simple mouse movements and keystrokes. However, they may fail to demonstrate human-like randomness in their behavior.
Impact: Third-generation bots are used for account takeover, application DDoS, API abuse, carding and ad fraud, among other purposes.
Mitigation: Third-generation bots are difficult to detect based on device and browser characteristics. Interaction-based user behavioral analysis is required to detect such bots, which generally follow a programmatic sequence of URL traversals.
The latest generation of bots have advanced human-like interaction characteristics — including moving the mouse pointer in a random, human-like pattern instead of in straight lines. These bots also can change their UAs while rotating through thousands of IP addresses. There is growing evidence that points to bot developers carrying out “behavior hijacking” — recording the way in which real users touch and swipe on hijacked mobile apps to more closely mimic human behavior on a website or app. Behavior hijacking makes them much harder to detect, as their activities cannot easily be differentiated from those of real users. What’s more, their wide distribution is attributable to the large number of users whose browsers and devices have been hijacked.
Impact: Fourth-generation bots are used for account takeover, application DDoS, API abuse, carding and ad fraud.
Mitigation: These bots are massively distributed across tens of thousands of IP addresses, often carrying out “low and slow” attacks to slip past security measures. Detecting these bots based on shallow interaction characteristics, such as mouse movement patterns, will result in a high number of false positives. Prevailing techniques are therefore inadequate for mitigating such bots. Machine learning-based technologies, such as intent-based deep behavioral analysis (IDBA) — which are semi-supervised machine learning models to identify the intent of bots with the highest precision — are required to accurately detect fourth-generation bots with zero false positives.
Such analysis spans the visitor’s journey through the entire web property — with a focus on interaction patterns, such as mouse movements, scrolling and taps, along with the sequence of URLs traversed, the referrers used and the time spent at each page. This analysis should also capture additional parameters related to the browser stack, IP reputation, fingerprints and other characteristics.