Malicious Traffic Detection via IP-Domain Dissociation
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Solution Overview
Problem
Current methods for detecting and mitigating malicious traffic, such as DDoS attacks, often inadvertently impact legitimate users due to reliance on IP address-based blocking and rate limiting, which can be circumvented by attackers using multiple IP addresses or shared IP addresses.
Innovation Solution
Implementing a system that dissociates IP addresses from a domain, logs malicious traffic, and uses machine learning to identify patterns for blocking requests, allowing for dynamic rule creation and application at the application layer, thereby reducing false positives and improving detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If IP address-based blocking and rate limiting are used to mitigate malicious traffic, then attack effectiveness is reduced, but legitimate users sharing the same IP address are negatively impacted
Solution Approach 1:
The system segments the identification basis from coarse-grained IP addresses to fine-grained behavioral patterns. By analyzing individual request characteristics, user agents, and traffic patterns rather than blocking entire IP ranges, the system can isolate malicious actors from legitimate users sharing the same network infrastructure.
Solution Approach 2:
The system applies different evaluation criteria to different traffic sources by analyzing local characteristics of each request. Legitimate users and malicious actors are differentiated based on their specific request patterns, timing, and behavior rather than their network origin, allowing selective mitigation that preserves legitimate traffic.
2Reliability
If rate limiting is applied to an IP address to prevent attacks, then the attack is partially mitigated, but all users from that IP address experience service degradation
Solution Approach 1:
The system dynamically adjusts mitigation measures based on real-time analysis of traffic patterns. Rather than applying static rate limits to entire IP addresses, the system continuously evaluates request characteristics and adapts blocking decisions individually, allowing legitimate users to maintain normal service levels while attackers are selectively restricted.
Solution Approach 2:
The system changes the parameters used for traffic evaluation from network-layer IP addresses to application-layer behavioral parameters. By monitoring request frequency, timing patterns, user agent consistency, and other behavioral metrics, the system can impose differentiated rate limits that affect only malicious traffic while preserving service for legitimate users.
3Adaptability or versatility
If attackers distribute attacks across multiple IP addresses to bypass mitigation, then detection difficulty increases, but the system can still identify coordinated malicious patterns
Solution Approach 1:
The system merges multiple individual request analyses into a coordinated pattern recognition framework. By combining information from multiple sources including request timing, user agent patterns, behavioral consistency, and traffic correlations across different IP addresses, the system can identify coordinated attacks even when distributed across many sources that would individually appear legitimate.
Data Source
AI summary
Methods and systems are presented for detecting and automatically blocking malicious traffic directed at a service provider. An IP address associated with a domain of the service provider is dissociated from the domain. Requests addressed to the IP address after it has been dissociated are identified as malicious and logged. IP addresses from which the malicious requests originated are blocked, and the log of malicious requests is used to train a model for determining pattern-based rules. Rules for managing traffic are determined based on the patterns and pushed to nodes of a proxy service, and the nodes may block or otherwise limit requests based on the rules.