Network Infrastructure Detection via Recursive Address Analysis
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Solution Overview
Problem
Current network security systems face challenges in detecting and mitigating malicious network infrastructure, as malicious actors frequently change their infrastructure to evade detection, making it difficult to differentiate between benign and malicious network activity.
Innovation Solution
The system automatically tracks and analyzes network infrastructure by identifying relationships between known malicious entities and unknown infrastructure, generating metadata to determine potential malicious communication patterns, and assigning risk labels based on various factors to block malicious traffic effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network security systems use traditional detection methods to identify malicious infrastructure, then detection accuracy is maintained for known threats, but the ability to detect new malicious infrastructure changes frequently is lost
Solution Approach 1:
The system performs preliminary actions by proactively identifying and analyzing relationships among network infrastructure components (IP addresses, domains, email addresses, name servers) before malicious activity occurs. It builds a knowledge graph of infrastructure relationships in advance, enabling rapid detection of new malicious infrastructure by comparing against the pre-established relationship patterns, thus resolving the contradiction between maintaining detection accuracy and adapting to new threats.
2Measurement precision
If network security analysts manually investigate potential security vulnerabilities and malicious attacks, then detection precision is improved, but the time required for analysis and response increases significantly
Solution Approach 1:
The system implements self-service by automatically performing security analysis functions that traditionally required manual analyst intervention. It autonomously investigates potential security vulnerabilities, analyzes malicious attacks, differentiates between benign and malicious network activity, and formulates response strategies using automated relationship analysis and risk scoring, thereby maintaining high detection precision while eliminating time loss associated with manual analysis.
3Difficulty of detecting and measuring
If the system analyzes relationships among all network infrastructure to identify malicious entities, then detection capability is improved, but the complexity of the analysis system increases
Solution Approach 1:
The system applies segmentation by breaking down the complex task of malicious infrastructure detection into distinct analytical components: extracting infrastructure metadata, building relationship graphs, identifying relationship patterns, calculating risk scores, and generating blacklists. Each component handles a specific aspect of the analysis independently, improving overall detection capability while managing system complexity through modular organization of the analysis process.
Data Source
AI summary
Network infrastructure can be automatically detected. A network sensor detects a new network message. A source-address of the new network message is extracted. A plurality of addresses are assembled based on the source-address. These are recursed, using each of the unique similar-addresses as current addresses. Metadata is assembled for each of the addresses in the plurality of addresses. For each particular address in the plurality of addresses, a risk-label is assigned out of a plurality of possible risk-labels, by weighing a plurality of factors; and performing a network security action with the risk-label.