Rotating IP Address Defense via ASN Correlation Analysis
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Solution Overview
Problem
Existing security systems struggle to effectively detect and prevent computing attacks that utilize IP address rotation by malicious entities, as these techniques obscure the identity and nature of the attacks.
Innovation Solution
The implementation of a rotating IP address protection framework that analyzes correlations between IP addresses and computing logs to identify IP address rotation, and employs various detection and prevention methods such as ASN ratio analysis, manual challenges, and dynamic status code returns to mitigate such attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If malicious entities rotate through multiple IP addresses during computing attacks, then the difficulty of detecting and measuring the attack source increases, but the ability to mask identity and nature of attacks improves
Solution Approach 1:
The system segments the analysis by dividing IP addresses into groups based on Autonomous System Numbers (ASNs). Instead of analyzing individual IP addresses in isolation, the framework groups them by ASN to identify patterns of rotation. This segmentation allows detection of coordinated attacks even when individual IP addresses change, as multiple IPs from the same ASN exhibiting similar behavioral patterns can be identified as part of the same malicious entity.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring and analyzing the ratio of successful to failed requests across grouped IP addresses. When the ASN ratio (successful requests divided by total requests) falls below a threshold, the system triggers protective actions. This feedback loop enables dynamic adjustment of security responses based on real-time analysis of attack patterns, allowing the system to adapt to ongoing IP rotation strategies.
2Measurement precision
If security systems implement comprehensive analysis of IP address correlations and computing logs, then the detection accuracy of IP address rotation improves, but the computational complexity and processing time increase
Solution Approach 1:
The framework extracts only the most critical identifying feature from IP addresses - the Autonomous System Number (ASN). Instead of analyzing all possible attributes of IP addresses and computing logs, the system focuses specifically on ASN correlation combined with request success/failure patterns. This extraction of essential features reduces computational complexity while maintaining detection accuracy, as ASN provides a stable identifier that persists across IP address rotations within the same malicious network.
Solution Approach 2:
The system performs preliminary grouping of IP addresses by ASN before conducting detailed analysis. By pre-organizing IP addresses into ASN-based groups and calculating baseline success/failure ratios in advance, the framework reduces the computational burden during real-time attack detection. This preliminary structuring allows for faster query and analysis during actual security incidents, as the heavy lifting of organization and initial metric calculation has already been completed.
3Reliability
If service providers implement protective measures such as blocking IP addresses during attacks, then the defense capability against computing attacks improves, but the risk of blocking legitimate users increases
Solution Approach 1:
The system merges multiple indicators before taking protective action: ASN group composition, request success/failure ratios, time-based patterns, and behavioral consistency across multiple IP addresses. Instead of blocking based on a single criterion, the framework combines these diverse signals to form a composite assessment of malicious activity. This merging approach increases reliability by requiring convergence of multiple independent indicators before triggering blocks, thereby reducing false positives while maintaining strong defense capability.
Solution Approach 2:
The protective measures are implemented dynamically rather than statically. The system continuously adjusts blocking decisions based on real-time analysis of ASN ratios and attack patterns. When suspicious activity is detected, the system can dynamically challenge specific IP addresses or ASN groups with additional verification steps before implementing blocks. This dynamic approach allows legitimate users with unusual but valid traffic patterns to be challenged rather than immediately blocked, while truly malicious actors facing consistent failure patterns are efficiently blocked.
Data Source
AI summary
Computing system defenses to rotating IP addresses by malicious entities during computing attacks are disclosed. An online entity may utilize a framework having computing operations for detecting and protecting from computing attacks using IP address rotation through multiple IP addresses to hide the malicious conduct. The threat detection system and framework may perform processes that indicate whether IP addresses are correlated and being used in the same computing operations, which may be malicious or fraudulent. If correlated, the framework may further determine that the IP addresses are being used to perpetrate the same or similar computing attack from a malicious actor. The framework may the execute one or more processes to protect from the computing attack that uses the rotation of IP addresses, including IP address blocking, manual challenges, and changing status code identifiers for webpage access requests.