Behavior Signature Analysis for Automated Bot Threat Response
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Solution Overview
Problem
Current identity and security systems are inefficient and imperfect, allowing bot access, scraping, and credential theft, and require extensive manual intervention and network resources, failing to combat the increasing complexity of bots and denial-of-service attacks.
Innovation Solution
A system that collects signals from web browsers, devices, and networks, generates behavior signatures, and analyzes them using machine learning to determine malicious access attempts, implementing automated security responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional identity and security systems are implemented, then security coverage is provided, but the systems require extensive manual intervention and significant network resources
Solution Approach 1:
The system performs automated bot identification and threat analysis without requiring manual intervention. The signal collector automatically gathers browser, device, and network signals, the parser processes them, the signature generator creates behavior signatures, and the responder implements security responses autonomously, making the security system self-sufficient
Solution Approach 2:
The patent replaces manual security operations with automated computational processes. Instead of human analysts examining security signals, machine learning models and automated algorithms analyze behavior signatures and determine malicious access attempts, substituting mechanical human labor with electronic processing systems
2Reliability
If conventional identity and security systems are implemented, then security coverage is provided, but significant network resources such as memory and bandwidth are consumed
Solution Approach 1:
The system extracts only the essential signals needed for bot identification from the overwhelming amount of available data. The signal collector selectively gathers browser, device, and network signals, and the parser extracts relevant features, focusing computational resources on critical information rather than processing all possible data
Solution Approach 2:
The system transforms raw security signals into compressed behavior signatures that capture essential malicious patterns in a condensed form. This parameter transformation reduces the data volume requiring network transmission and processing while maintaining the ability to identify threats
3Reliability
If conventional identity and security systems are implemented, then basic security is provided, but bot access, scraping, and credential theft are still allowed
Solution Approach 1:
The system continuously monitors access attempts, analyzes behavior signatures, and implements security responses that feed back into the system. The responder module uses analysis results to block malicious access, and the system learns from new attack patterns, creating a dynamic feedback loop that adapts to evolving bot threats
Solution Approach 2:
The system performs preliminary analysis of access attempts by generating and analyzing behavior signatures before allowing or blocking access. This preliminary security check identifies malicious bots early in the access process, preventing them from reaching protected resources
4Measurement precision
If automated bot identification is implemented, then bot detection capability is improved, but the complexity of analyzing behavior signatures increases
Solution Approach 1:
The system divides the complex analysis task into distinct modular components: signal collection, parsing, signature generation, analysis, and response implementation. Each module handles a specific aspect of the analysis process, making the overall complex system manageable through functional segmentation
Data Source
AI summary
Methods and systems consistent with the disclosure can include receiving, from a web browser executed on a consumer electronic device and by a server executing an application, a request to perform a threat analysis of an access attempt; collecting, through a signal collector of the application, a signal from a browser, a signal from the consumer electronic device, a signal from a network, and an interaction signal; aggregating, through an aggregator of the application, the collected signals into a database, parsing, through a parser of the application, the collected signals; generating, through a signature generator of the application, a behavior signature from the collected signals; analyzing, through an analyzer of the application, the behavior signature; determining, through the analyzer of the application, whether the access attempt is malicious; and implementing, through a responder of the application, a security response on the web browser based on the access attempt being malicious.