Malicious Stockpiled Domain Classification Before Botnet Deployment
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Solution Overview
Problem
Cybercriminals are increasingly using sophisticated techniques to obfuscate malicious domains, making it difficult for security platforms to detect and prevent the deployment of malicious stockpiled domains, which can be used to compromise computer systems and form botnets.
Innovation Solution
A system and method for detecting malicious domains using fingerprinting, heuristics-based, and machine learning classifications, trained on domain data from datasets such as certificate logs, passive DNS, and WHOIS data, to identify stockpiled domains before they are used for malicious activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional security methods are used to detect malicious domains, then detection capability is limited, but false positives increase and detection accuracy decreases due to sophisticated obfuscation techniques
Solution Approach 1:
The detection system is segmented into multiple specialized components: fingerprinting module for domain signature extraction, heuristics engine for rule-based analysis, machine learning classifier for pattern recognition, and threat intelligence integrator. Each component handles specific aspects of domain analysis, improving overall detection accuracy while distributing system complexity across modular units
Solution Approach 2:
The patent combines multiple detection approaches (fingerprinting, heuristics, machine learning, and threat intelligence) into a composite detection system. This multi-layered approach integrates different methodologies to achieve superior detection accuracy that exceeds the capability of any single method alone
2Measurement precision
If comprehensive domain analysis is performed to improve detection accuracy, then detection precision improves, but processing time increases
Solution Approach 1:
The system performs preliminary analysis by extracting domain fingerprints and applying heuristic rules before invoking computationally intensive machine learning models. Quick fingerprint matching and rule-based filtering eliminate obviously malicious or benign domains early, reserving detailed analysis only for borderline cases that require deeper inspection
Solution Approach 2:
The detection system applies different levels of analysis depth based on risk assessment. For low-risk domains, only lightweight fingerprinting and heuristic checks are performed. For high-risk or ambiguous domains, the system escalates to full machine learning analysis, optimizing the balance between detection precision and processing time
3Reliability
If multiple classification methods are used to improve detection reliability, then detection reliability improves, but system complexity increases
Solution Approach 1:
The patent introduces a domain classification intermediary layer that sits between raw domain data and final detection results. This intermediary orchestrates multiple classification methods (fingerprinting, heuristics, machine learning) and synthesizes their outputs into a unified reliability score, managing the complexity of coordinating multiple methods while improving overall detection reliability
Data Source
AI summary
The present application discloses a method, system, and computer system for detecting stockpiled domains. The method includes (i) determining that a candidate domain is a malicious stockpiled domain using one or more of (a) a fingerprinting classification, (b) a heuristics-based classification, and (c) a machine learning classification, and (ii) applying a security policy based on a classification of the candidate domain as the malicious stockpiled domain.