Network Reputation Management for Malware Detection
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Solution Overview
Problem
Existing methods for detecting new threats such as spam emails and viruses are becoming less effective as malicious content producers design programs to avoid detection, leading to inefficiencies in malware detection systems.
Innovation Solution
A system that derives a reputation for network addresses by analyzing high-level email features like domain registration analysis, hashed term frequency indexing, persistent communication patterns, address age, correlation analysis, and hash vault matching, using a TrustedSource server to compile and classify domain names and IP addresses for improved security determinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If signature-based detection methods are used to identify known threats, then detection accuracy for known malware is improved, but detection effectiveness against new threats deteriorates
Solution Approach 1:
The system performs preliminary actions by proactively gathering and analyzing multiple indicators (domain registration data, email headers, message content, IP address history) before malicious activity occurs. Reputation scores are pre-calculated and stored, enabling rapid identification of new threats without requiring prior signatures. This allows the system to detect novel malware and spam by comparing against pre-established reputation baselines rather than waiting for signature updates.
Solution Approach 2:
The invention transforms the detection approach by changing from static signature matching to dynamic reputation scoring based on multiple parameters including domain age, registration information, email sending patterns, and historical behavior. These parameter changes enable the system to adapt to new threats by evaluating multiple attributes simultaneously, allowing detection of previously unknown malware and spam variants through their behavioral characteristics rather than requiring exact signature matches.
2Measurement precision
If multiple analysis features are combined to improve threat detection accuracy, then detection precision is improved, but system complexity increases
Solution Approach 1:
The system segments the complex threat detection task into distinct analytical components: domain registration analysis, email header validation, message content scanning, IP address reputation checking, and sending pattern analysis. Each segment processes specific data types independently and contributes to an overall reputation score. This segmentation reduces system complexity by organizing multiple analysis features into modular, manageable units that can be processed in parallel and combined through weighted scoring.
3Speed
If real-time analysis of multiple email features is performed, then responsiveness to new threats is improved, but processing time increases
Solution Approach 1:
The system merges multiple analysis operations into a unified reputation scoring process. Instead of performing separate sequential analyses of domain registration, email headers, content, and sending patterns, the system combines these evaluations into a single integrated reputation score calculation. This merging allows parallel processing of multiple features and enables real-time threat detection by delivering comprehensive analysis results in one operation rather than through multiple sequential steps.
Data Source
AI summary
A system derives a reputation for a plurality of network addresses, the reputation of each network address determined by analyzing a plurality of high-level email features related to one or more emails originating from the network address. The plurality of high-level email features include domain registration analysis, hashed term frequency indexing, persistent communication, address age, correlation analysis, zombie detection, and hash vault matching.