Inline DNS Request Interception for Fast Flux Network Blocking
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Solution Overview
Problem
Fast flux networks, used by botnets to hide phishing and malware delivery sites, are challenging to detect and block due to their dynamic IP address changes, making existing network security systems inefficient in real-time detection and prevention.
Innovation Solution
A network security system that learns normal domain names over a time frame, creates a whitelist and blacklist, and intercepts DNS requests to block unknown domain names, allowing for rapid evaluation and blocking of suspicious requests without prior knowledge of DNS names used by fast flux networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If statistical analysis of large data sets is used to detect fast flux networks, then detection capability is improved, but processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-processing DNS query data during a learning phase to extract statistical features (query frequency, response time, TTL values) and store them in data structures for rapid access. This allows the detection phase to quickly compare new queries against pre-computed statistics without performing full statistical analysis in real-time, resolving the contradiction between thorough detection and fast processing.
2Measurement precision
If comprehensive packet inspection is performed at multiple protocol layers, then security detection accuracy is improved, but system complexity increases
Solution Approach 1:
The patent extracts and focuses inspection on the most critical indicators of fast flux networks: DNS query domain names, response TTL values, and query frequency patterns. By taking out only these essential elements from comprehensive multi-layer packet inspection, the system maintains high detection accuracy for fast flux networks while significantly reducing system complexity and processing overhead.
3Reliability
If real-time blocking of unknown domain names is implemented, then prevention effectiveness is improved, but false positive rate increases
Solution Approach 1:
The patent implements feedback mechanisms where blocked DNS queries are logged and analyzed to refine the detection model. The system learns from blocked queries by adjusting statistical thresholds and updating the understanding of normal vs. malicious patterns. This feedback loop allows the system to improve prevention effectiveness over time while reducing false positives by adapting to legitimate traffic patterns that may have been initially blocked.
Data Source
AI summary
Example embodiments disclosed herein relate to a network security system. The network security system intercepts inline DNS requests. It is determined whether a domain name associated with one of the inline DNS requests corresponds with one or more domain names. A security action is performed based on the determination.