Deep Learning Bot Detection for Changing Attack Scripts
Find Innovative SolutionsGenerate Solutions
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 strategies.
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 by buffering and normalizing incoming data, pruning irrelevant samples, and implementing a bot matcher/filter stage to classify transactions effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning is applied to bot detection with static class assumptions, then classification accuracy for known bot types is improved, but adaptability to new and modified bot scripts deteriorates
Solution Approach 1:
The patent transforms the static class assumption into a dynamic learning process where the neural network continuously adapts to new bot patterns. Instead of fixing classes beforehand, the system dynamically learns new bot types through ongoing training with freshly collected data, allowing the classifier to evolve alongside attacker techniques while maintaining high detection accuracy
Solution Approach 2:
The system implements self-service through automated data collection and model retraining without human intervention. The neural network serves itself by automatically collecting new transaction data, identifying patterns of modified bot scripts, retraining on this fresh data, and deploying updated classification models, thereby maintaining both accuracy and adaptability autonomously
2Reliability
If traditional bot detection methods are used, then detection of known bot patterns is effective, but detection of modified bot scripts deteriorates
Solution Approach 1:
The patent implements feedback loops where detection results from modified bot scripts feed back into the training process. When the system encounters new or altered bot patterns, it collects this data, retrains the neural network with these fresh examples, and applies the updated model to improve detection of similar modified scripts in the future, creating a continuous improvement cycle
Solution Approach 2:
The system performs preliminary actions by proactively collecting and analyzing data from modified bot scripts before they become widespread threats. By continuously monitoring and retraining on emerging patterns, the system prepares detection models in advance, enabling earlier and more effective identification of modified bot variants
3Measurement precision
If manual updates to detection models are performed, then detection accuracy can be maintained, but response time to new threats increases
Solution Approach 1:
The system eliminates manual intervention by implementing self-service automation for the entire model update pipeline. Automated scripts continuously collect new transaction data, identify patterns from modified bots, retrain the neural network, and deploy updated models without human involvement, thereby maintaining high detection accuracy while dramatically reducing response time to emerging threats
Solution Approach 2:
The patent ensures continuous model improvement through uninterrupted data collection and retraining operations. Rather than periodic manual updates, the system maintains a continuous cycle of learning from new bot patterns and updating detection models, ensuring detection accuracy is maintained constantly while responding immediately to new threats as they emerge
Data Source
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.