Scripted Activity Classification via Challenge-Response Fingerprinting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods are inadequate in detecting and classifying scripted activity in computer systems, which is a significant challenge in preventing and mitigating cyberattacks, as automated scripts are often used to bypass security measures and perpetrate crimes efficiently.
Innovation Solution
The implementation of a system that uses questioning and fingerprinting techniques to categorize applications as human-operated, automated scripts, or a combination of both, by issuing challenges and analyzing the responses, reputation scores, and monitored activity to determine the trustworthiness and classify the activity as automated, semi-automated, or manual.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated scripts are used to perform security checks and activity monitoring, then detection accuracy and response time are improved, but system complexity and computational resource requirements increase
Solution Approach 1:
The system segments the detection process into multiple independent components: challenge generation module, fingerprinting module, reputation scoring module, and classification module. Each module performs a specific function and can be independently optimized, reducing overall system complexity while maintaining high detection accuracy through coordinated operation of specialized subsystems.
Solution Approach 2:
The system performs preliminary fingerprinting and reputation scoring during the challenge-response phase before actual activity monitoring begins. This preliminary characterization allows the system to establish baseline trust levels and detection thresholds in advance, reducing computational burden during real-time monitoring while maintaining accurate classification.
2Measurement precision
If multiple detection methods (questioning, fingerprinting, reputation scoring) are combined, then classification accuracy is improved, but processing time and computational overhead increase
Solution Approach 1:
The system implements tiered detection where not all detection methods are applied uniformly to all applications. Low-risk applications may receive basic fingerprinting only, while high-risk or suspicious applications trigger full multi-method analysis including questioning, detailed fingerprinting, and reputation scoring. This partial application of detection methods reduces average processing time while maintaining high classification accuracy for suspicious cases.
Solution Approach 2:
The system uses feedback from initial detection results to dynamically adjust subsequent detection efforts. If fingerprinting results clearly indicate legitimate software, the system terminates further analysis quickly. If results are ambiguous or suspicious, the system automatically triggers additional questioning and reputation checks, optimizing processing time based on actual risk levels while maintaining accurate classification.
Data Source
AI summary
Methods, systems, and computer program products for accurately classifying a computer program interacting with a computer system using questioning and fingerprinting are provided. A computer-implemented method may include issuing a challenge to an application interacting with a computer system to determine whether activity performed by the application is scripted, analyzing data associated with a result of the challenge to determine whether the result is at least partially correct, and computing a reputation score for the application based on the result of the challenge. A computer-implemented method also may include comparing the reputation score for the application to a threshold for identifying non-scripted activity, and determining that the activity performed by the application is scripted when the reputation score for the application does not meet the threshold for identifying non-scripted activity.