DNS Traffic Analyzer for Network Anomaly Detection
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Solution Overview
Problem
In computer networks, detecting anomalies caused by malicious software, malfunctioning hardware, or misconfigured devices can be challenging due to the complexity and size of private networks, leading to potential prolonged malicious activity going undetected.
Innovation Solution
Implementing a system that captures and analyzes DNS query packets using a DNS traffic analyzer trained with machine learning techniques to identify anomalies by filtering safe traffic with whitelists and blacklists, and analyzing packet characteristics such as query rates and configurations to detect deviations from standard network behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network monitoring is performed in large private networks, then network security is improved, but system complexity increases
Solution Approach 1:
The patent segments the large private network into multiple subnetworks or zones, each monitored by dedicated monitoring devices or software agents. This division allows comprehensive security coverage while managing complexity through modular deployment, where each segment can be independently configured and maintained.
Solution Approach 2:
The patent introduces intermediary components such as network controllers, security information and event management (SIEM) systems, or centralized monitoring servers that aggregate data from multiple sources. These intermediaries simplify the overall system architecture by providing a unified interface for monitoring and response, reducing the complexity burden on individual monitoring points.
2Measurement precision
If anomaly detection sensitivity is increased, then detection accuracy is improved, but false positive rate increases
Solution Approach 1:
The patent implements dynamic threshold adjustment and adaptive sensitivity settings that automatically calibrate based on learned normal network behavior patterns. The system dynamically adjusts detection parameters in response to changing network conditions, maintaining high detection accuracy while adapting to legitimate traffic variations that would otherwise trigger false positives.
Solution Approach 2:
The patent incorporates feedback mechanisms where detected anomalies are analyzed and used to refine detection models. True positives reinforce sensitive detection rules, while false positives trigger rule adjustments or threshold modifications. This continuous feedback loop maintains high detection accuracy while systematically reducing false positive rates over time.
Data Source
AI summary
Examples relate to detecting network anomalies. In one example, a computing device may: receive, from each of a plurality of packet capture devices of a private network, domain name system (DNS) query packets that were sent by a particular client computing device operating on the private network, each DNS query packet specifying i) a destination DNS server, ii) a query domain name, and iii) a source address that specifies the particular client computing device; provide at least one of the DNS query packets to a DNS traffic analyzer that is trained to identify DNS anomalies based on characteristics of the DNS query packets; receive anomaly output from the DNS traffic analyzer, the anomaly output indicating a DNS anomaly that was identified for the DNS query packets; and in response to receiving the anomaly output, provide a user device with data specifying the identified DNS anomaly.