DNS SOC Mass-Spreading Detection With Automated Threat Response
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Solution Overview
Problem
There is an increasing number of DNS-related security threats and attacks targeting enterprise networks, necessitating improved techniques for DNS security operations center insights to enhance threat detection and mitigation.
Innovation Solution
A DNS Security Operations Center (SOC) insights platform that collects DNS security events, generates insights, and performs automated actions, including mass spreading detection, notification, and automated responses, utilizing machine learning and data correlation to identify and mitigate threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If DNS security events are collected and analyzed manually, then detection accuracy can be maintained, but response time increases and productivity decreases
Solution Approach 1:
The patent replaces manual DNS security analysis with automated machine learning models and algorithms that process DNS queries, detect anomalies, and identify threats without human intervention, thereby maintaining detection accuracy while dramatically improving response speed
Solution Approach 2:
The patent introduces an automated analysis system with machine learning models as an intermediary between DNS event collection and threat detection, enabling high-speed automated processing while preserving detection accuracy through trained algorithms
2Reliability
If comprehensive DNS security monitoring is implemented, then threat detection capability improves, but system complexity increases
Solution Approach 1:
The patent divides the DNS security monitoring system into modular components including DNS query collectors, machine learning analysis engines, anomaly detection modules, and response systems, allowing comprehensive monitoring while managing complexity through organized segmentation
Solution Approach 2:
The patent creates a multi-functional automated analysis system that performs multiple security functions (anomaly detection, threat identification, pattern recognition, and response automation) through integrated machine learning models, reducing overall system complexity while enhancing detection capability
3Productivity
If automated actions are implemented in response to DNS threats, then response speed improves, but false positive impact increases
Solution Approach 1:
The patent implements feedback mechanisms where automated actions and their outcomes are monitored and fed back into the machine learning models, allowing the system to learn from false positives and adjust its detection thresholds to reduce future false alarms while maintaining rapid response capability
Solution Approach 2:
The patent applies automated actions selectively based on confidence thresholds and risk levels, performing full automated responses only for high-confidence threats while using partial automation or manual review for lower-confidence cases, thereby reducing false positive impact while maintaining fast response for critical threats
Data Source
AI summary
Various techniques for DNS security operations center insights for mass spreading detection are disclosed. In some embodiments, a system/process/computer program product for DNS security operations center insights for mass spreading detection includes collecting Domain Name System (DNS) security associated events; generating a plurality of insights based on the collected DNS security associated events, wherein at least one of the plurality of insights includes a mass spreading detection insight; and performing an action based on one or more of the insights including the mass spreading detection insight.