Detecting bots is crucial to maintaining consumer trust and protecting brand reputation. If unidentified, malicious bots can steal value from users through ad fraud, click spamming, credential stuffing and carding attacks that lead to refunds, chargebacks, legal fees and loss of stock value.

To protect against these threats, businesses need a robust and adaptive solution to bot behavior detection and classification. This requires a multifaceted approach that uses techniques such as machine learning, behavioral analysis and device fingerprinting to identify both malicious and good bots.

Understanding Bot Behavior Detection and Classification Techniques

Traditional methods of detecting bots are based on analyzing user behavior such as mouse movements, keystroke patterns, and navigation flow. These methods are often unable to distinguish between good and bad bots, particularly when the latter use techniques like spoofing or VPNs to bypass detection. Other techniques include examining IP addresses to identify suspect or known bots and using captchas, which can be bypassed by sophisticated bots.

The best bot detection techniques are those that are robust against adversarial adaptation. This means balancing “easy wins” that catch current bot behaviors with fundamental signals that remain useful as bots evolve. It also involves a rigorous process of feature engineering that balances the trade-offs between identifying a user as human versus identifying a user as a bot. For example, a sensitivity threshold should be set that minimizes the risk of falsely labeling a human as a bot while ensuring that the vast majority of users will be identified as humans.