Fingerprinting Cookie Mechanism for Cyber-Attack Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Cyber-attacks are difficult to detect and prevent due to attackers hiding their identities and using legitimate user information, making it challenging to differentiate between legitimate and malicious requests, especially in automated attacks that mimic normal user behavior.
Innovation Solution
A cyber-attack detection system that analyzes request paths and user identities, uses fingerprinting cookies to track users, and employs verification processes such as CAPTCHA, SMS verification, and trusted execution environments to identify and block malicious requests, routing them to a honeypot if necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fingerprinting cookies are used to track users and verify identities, then the ability to detect malicious requests is improved, but the complexity of the system increases due to additional verification mechanisms
Solution Approach 1:
The patent introduces fingerprinting cookies as an intermediary mechanism that bridges the gap between user identification and attack detection. The cookies store unique identifiers that allow the system to track user behavior patterns without requiring complex real-time analysis of each request, thereby improving detection accuracy while managing system complexity through a standardized tracking mechanism
Solution Approach 2:
The system changes the parameter of user identification from anonymous IP-based tracking to cookie-based fingerprinting. This parameter change enables more reliable user identification across multiple requests, improving the precision of detecting malicious patterns while the cookie mechanism provides a scalable solution that doesn't linearly increase system complexity
2Reliability
If verification processes like CAPTCHA and SMS verification are implemented, then the reliability of user authentication is improved, but the time required for request processing increases
Solution Approach 1:
The system performs preliminary actions by implementing fingerprinting cookie tracking before attacks occur. By establishing user identity markers in advance through cookies, the system can quickly match subsequent requests to known users without requiring time-consuming verification processes like CAPTCHA or SMS for every interaction, thus maintaining high authentication reliability while reducing processing time for legitimate users
Solution Approach 2:
The verification mechanism dynamically adapts based on user behavior patterns detected through cookie tracking. The system adjusts the level of verification required - implementing stricter checks only when malicious patterns are detected - thereby maintaining high authentication reliability while minimizing time loss for legitimate users who don't trigger verification thresholds
3Productivity
If automated attack detection is implemented, then the productivity of security monitoring is improved, but the difficulty of detecting and measuring malicious activity increases due to identity spoofing
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring request patterns associated with fingerprinting cookies and using this information to improve future detection accuracy. The automated analysis of cookie-tracked behavior provides feedback loops that enhance the system's ability to detect spoofing attempts, improving security monitoring productivity while progressively reducing detection difficulty through learned patterns
Solution Approach 2:
The system creates copies of user identifiers through fingerprinting cookies that persist across different sessions and devices. This copying mechanism allows the system to track and analyze user behavior patterns without being thwarted by identity spoofing, as the cookie-based identifier provides a consistent reference point for automated detection algorithms, thereby improving monitoring efficiency while managing detection complexity
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for detecting cyber-attack. In an embodiment, a server receives a request to an application from a user device. The server determines that there is no cookie in the received request. The server then generates a new fingerprinting cookie and sends a verification request to the user device to verify the identity of a user. When the server receives the verification reply from the user device, the server determines that the verification reply is valid, marks the new cookie as a verified cookie, and transfers the request to the application for processing. The server can also unverify the verified cookie when the verified cookie is included in a malicious request. The server can determine that a request is malicious by analyzing functions the user wishes to perform using the request.