ML Bot Attack Detection With Adaptive Traffic Rules
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Solution Overview
Problem
Conventional web security systems fail to effectively and efficiently detect and mitigate sophisticated bot attacks, particularly those employing tactics like browser fingerprint spoofing and IP rotation, due to their reliance on manual analysis and static signature-based policies.
Innovation Solution
A machine learning-based security system that trains algorithms on-the-fly to identify unique features of bot attacks, generating tailored security policies to block such attacks by analyzing request and response data, and automatically adapting to evolving attack strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis and static signature-based policies are used to detect bot attacks, then the security system is simple to implement, but the detection precision and response time are insufficient against sophisticated attacks
Solution Approach 1:
The patent transitions from static signature-based policies to dynamic machine learning models that continuously learn and adapt to new attack patterns. The system automatically updates detection rules based on real-time traffic analysis, enabling it to respond to evolving bot attack strategies without manual intervention.
Solution Approach 2:
The security system performs self-training and self-adjustment through automated machine learning workflows. The system automatically extracts features from traffic data, trains classification models, and generates updated security policies without requiring manual analysis, thereby improving detection precision while managing complexity through automation.
2Measurement precision
If machine learning models are trained on comprehensive traffic data to improve detection accuracy, then the detection precision increases, but the training time and computational resources increase
Solution Approach 1:
The system performs preliminary feature extraction and data preprocessing on traffic data before model training. By preparing the data in advance and organizing it into relevant features, the system reduces the computational burden during the actual training phase, enabling faster model development while maintaining high detection accuracy.
Solution Approach 2:
The patent implements automated workflows that rapidly iterate through model training and evaluation cycles. The system quickly trains multiple models with different parameters and selects the best performing ones, reducing the overall time investment while achieving high detection accuracy through efficient computational strategies.
3Reliability
If the security system continuously monitors and analyzes all traffic data in real-time, then the detection capability improves, but the processing speed and system performance decrease
Solution Approach 1:
The system extracts only the most relevant features from complete traffic data for analysis. By identifying and focusing on key indicators of bot attacks rather than processing all raw data, the system maintains high detection capability while significantly reducing the computational load and improving processing speed.
Solution Approach 2:
The patent applies partial analysis by focusing computational resources on suspicious or anomalous traffic patterns rather than uniformly analyzing all traffic. The system identifies and prioritizes traffic that exhibits characteristics of bot attacks, applying more intensive analysis only where needed, thereby maintaining reliability while improving overall processing efficiency.
Data Source
AI summary
Various embodiments include a system that utilizes machine learning to detect and mitigate bot attacks. The system comprises processing circuitry. The processing circuitry obtains historical traffic data and attack traffic data in response to an attack notification. The attack traffic data characterizes traffic received during a bot attack and the historical traffic data characterizes other traffic received when the bot attack is not occurring. The processing circuitry extracts features from the historical traffic data and the attack traffic data. The processing circuitry trains a machine learning classifier to identify the features that correspond to attack traffic and the features that correspond to legitimate traffic. The processing circuitry forms decision rules based on an output from the machine learning classifier to block the attack traffic based on the features that correspond to the attack traffic. The processing circuitry generates one or more security policies based on the decision rules.