Web Bot Detection Using Dynamic Deep Learning Retraining
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Solution Overview
Problem
Existing deep learning-based bot detection systems struggle to adapt to changing bot scripts, as they assume static classes, leading to ineffective classification when attackers modify their attack methods.
Innovation Solution
A bot detection system that leverages deep learning to automatically retrain on new attack vectors, using a neural network that learns to distinguish between human and bot transactions, even when attackers change their scripts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning is applied to bot detection with static class assumptions, then initial bot detection accuracy is improved, but the system becomes ineffective when attackers modify their scripts
Solution Approach 1:
The system implements dynamic retraining of the deep learning model using incoming transaction data in real-time. Instead of relying on static pre-collected bot samples, the system continuously adapts by incorporating new transaction patterns, allowing the classification model to evolve and maintain effectiveness against changing bot scripts while preserving detection accuracy.
2Reliability
If pre-collected human and bot data is used for training, then initial model performance is improved, but the system can only detect the specific class of bots it was trained on
Solution Approach 1:
The system maintains continuous learning by constantly retraining the model with new transaction data streams. This continuous action ensures that the model doesn't just rely on initial pre-collected data but continuously updates its knowledge base, maintaining reliability while expanding detection coverage to new bot types as they emerge.
Solution Approach 2:
The system implements feedback loops where incoming transaction data is continuously fed back into the training process. This feedback mechanism allows the model to learn from actual encountered bot patterns and human transactions, improving both reliability on known patterns and adaptability to new bot types through iterative refinement.
3Measurement precision
If manual data collection and model retraining is performed, then detection accuracy is maintained, but significant time and human intervention are required
Solution Approach 1:
The system implements self-service automation where the deep learning model automatically retrains itself using incoming transaction data without requiring manual data collection or human intervention. The system autonomously manages the entire retraining pipeline, maintaining detection accuracy while eliminating time losses associated with manual processes.
Solution Approach 2:
The system performs preliminary preparation by continuously pre-processing and preparing incoming transaction data for training in advance. This preliminary action ensures that when retraining is needed, the data is already ready, significantly reducing the time required for model updates while maintaining high detection accuracy.
Data Source
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AI summary
This disclosure describes a bot detection system that leverages deep learning to facilitate bot detection and mitigation, and that works even when an attacker changes an attack script. The approach herein provides for a system that rapidly and automatically (without human intervention) retrains on new, updated or modified attack vectors.