Elevating Anomaly-Based Bot Detection via Dynamic Threshold Management


In our previous post, we discussed the role of anomaly-based detection in identifying and mitigating sophisticated bot attacks. In this blog, we’re diving deeper into how Dynamic Threshold Management strengthens this approach by offering adaptive, real-time adjustments to detection thresholds. This tool enables a refined and responsive defense against evolving bot attacks, ensuring minimal false positives while maximizing detection accuracy.

While static thresholds are effective in most cases, a dynamic approach offers enhanced flexibility for responding to the changing tactics of bot behavior, adapting detection thresholds in real-time.

How Dynamic Threshold Management Works: A High-Level Overview

Dynamic Threshold Management is designed to work in tandem with modules that generate anomaly scores, such as our Time Series Anomaly Detection Model. Rather than relying on a single threshold, this module recalculates the optimal threshold with each cycle, based on real-time feedback from captcha responses and anomaly scores.

This adaptive approach allows Dynamic Threshold Management to continuously adjust based on the bot traffic observed. By recalculating the threshold dynamically, the module optimizes bot detection effectiveness without the drawbacks of a fixed threshold.

Key Components of Dynamic Threshold Management

Dynamic Threshold Management operates through several interrelated processes, each designed to ensure that bot sources are only flagged and blacklisted when the data suggests a high likelihood of malicious activity. Here’s an overview of its main components:

  • Isotonic Calibrator: The process begins with transforming raw anomaly scores into probability-based scores that more accurately reflect the likelihood of bot activity. The Isotonic Calibrator uses isotonic regression to map each score to an estimated probability. This approach divides scores into predefined bins, and for each bin, the calibrator collects evidence of how frequently bot-like activity is confirmed (such as through unsolved captchas). Each score in a bin is then assigned a probability that represents its likelihood of indicating bot behavior, a method that balances detection accuracy with interpretability. In practice, this calibration process allows for smooth adjustment over time and ensures that bot scores remain aligned with real-world feedback.

  • Threshold Generator: Based on the calibrated probability scores, the Threshold Generator dynamically determines the cut-off point for flagging bot sources. This calculation involves factoring in false positive (FP) allowances and observed captcha responses. In each cycle, the generator calculates an optimal threshold based on the current traffic pattern, adjusting for the number of permissible FPs remaining in a given period. This approach balances security and user experience by dynamically fine-tuning the threshold according to real-time bot traffic and legitimate user activity. Mathematically, the Threshold Generator resolves for an unknown threshold “t” by applying it to real-time data and ensuring FPs remain within acceptable bounds for that cycle.

  • Feedback Generator: This component continuously gathers captcha feedback, providing critical real-time insights to refine the threshold settings further. If captcha responses indicate that legitimate users are being mistakenly flagged more than expected, the Feedback Generator initiates a recalibration of the threshold, adjusting it downward to reduce false positives. This feedback loop allows the system to respond quickly to changing traffic patterns, particularly when bot activity spikes or adjusts its behavior. By actively using this feedback, Dynamic Threshold Management ensures minimal disruption for real users, preserving an optimal balance between security and user experience.

By working together, these components allow Dynamic Threshold Management to achieve high accuracy while minimizing disruption for actual users. This makes it an important part of Radware’s anomaly-based detection approach.

Conclusion

Dynamic Threshold Management is an integral part of Radware’s anomaly-based detection strategy, providing an adaptable layer that can keep up with the complex and evolving nature of bot activity. By leveraging real-time feedback and adjusting thresholds as needed, Dynamic Threshold Management helps balance effective bot detection with minimal disruption for legitimate users. Compared to static thresholds, this dynamic approach offers enhanced flexibility to respond to changing bot tactics, making it a valuable addition to Radware’s security offerings. This approach helps safeguard applications while maintaining a seamless user experience.

Rakesh Thatha

Rakesh Thatha

Rakesh Thatha is the Chief Technologist at Radware Innovation Center, overseeing the Cloud Application Security product lines and Cloud Architecture. An MS graduate from IIT Madras, he began his career as a cybersecurity researcher, publishing papers in top-tier conferences. With multiple patents in the fields of cybersecurity and artificial intelligence, he founded two cybersecurity startups, ArrayShield and ShieldSquare, building world-class products and R&D teams from scratch. ShieldSquare was acquired by Radware in 2019. Rakesh is also a regular speaker at cybersecurity and cloud conferences, sharing his expertise with the industry.

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