Autonomous Program Signature Generation via Real-Time Traffic Analysis
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Solution Overview
Problem
Existing systems struggle to effectively identify and classify autonomous programs in real-time due to their rapidly changing and multi-variate behavior, often relying on pre-populated databases that fail to account for contemporary variations.
Innovation Solution
The implementation of a method that utilizes real user measurements (RUMs) to analyze user-behavior and network fingerprints in real-time, leveraging machine learning and natural language processing to identify and classify autonomous programs, and updates an internal database through HTTP requests, enabling real-time detection and categorization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pre-populated databases of bot signatures are used for autonomous program identification, then the system structure is simple and easy to implement, but the system cannot keep up with rapidly changing and multi-variate behavior of contemporary autonomous programs
Solution Approach 1:
The patent implements dynamic signature generation by continuously analyzing network traffic patterns and user behavior metrics to create updated bot signatures in real-time, allowing the system to adapt to changing autonomous program behavior without requiring complete reconfiguration of the identification system
Solution Approach 2:
The system performs self-updating by automatically generating new bot signatures through analysis of collected network traffic data and user behavior metrics, eliminating the need for manual database updates and enabling autonomous adaptation to new bot variants
2Measurement precision
If real-time analysis of user behavior and network fingerprints is implemented, then identification accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent collects and pre-processes user behavior metrics and network traffic data during normal operation, preparing signature information in advance so that real-time identification can leverage pre-computed patterns without requiring intensive processing at the moment of detection
Solution Approach 2:
The system replaces traditional signature matching mechanisms with machine learning models that analyze user behavior patterns and network fingerprints, achieving higher identification accuracy through intelligent algorithms rather than simple pattern matching
3Measurement precision
If comprehensive user behavior metrics and network traffic data are collected, then classification accuracy improves, but data privacy concerns and compliance requirements increase
Solution Approach 1:
The patent extracts only the necessary behavioral patterns and traffic characteristics needed for bot identification from the collected data, separating essential classification features from unnecessary personal information, thereby maintaining accuracy while reducing privacy risks
Data Source
AI summary
Systems and methods for autonomous program signature generation may include one or more processor(s) that identify a client device executing an autonomous program based at least on traffic from a plurality of client devices. The processor(s) may classify the autonomous program into one or more classifications based on an attribute of the autonomous program. The processor(s) may store an association between the autonomous program and the one or more classifications. In some implementations, the processor(s) may receive a plurality of entries over a time window, corresponding to associations between respective autonomous programs executing on client devices and classification(s) of the autonomous program. The processor(s) may identify one or more features for a respective user agent corresponding to the autonomous program and a corresponding classification of the autonomous program. The processor(s) may train a machine learning model using the one or more features for each entry and the corresponding classification.