Malware Signature Generation Using Developer Fingerprints
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Solution Overview
Problem
Existing methods for generating malware signatures are inefficient, particularly when dealing with new malware variants or limited sample sets, as they require numerous known samples or manual expert analysis, leading to high false positives and inefficiencies.
Innovation Solution
The system generates malware signatures based on developer fingerprints extracted from debug information in binary executable files, using patterns and parsers to identify unique meta information, enabling automated signature creation even with limited samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods are used to generate malware signatures, then detection coverage can be achieved, but the process requires numerous known samples or manual expert analysis leading to high false positives and inefficiency
Solution Approach 1:
The patent extracts developer fingerprint information from debug symbols embedded in malware binaries. These fingerprints contain unique identifiers such as developer names, email addresses, and other metadata that can directly identify malware families without requiring numerous samples or manual analysis. This extraction approach resolves the contradiction by obtaining precise detection capabilities from limited data.
Solution Approach 2:
The patent performs preliminary extraction and analysis of developer fingerprints from available malware samples before signature generation. By pre-processing the debug information to isolate unique developer identifiers, the system prepares detection-ready data in advance, eliminating the need for subsequent manual expert analysis and reducing false positives while maintaining high efficiency.
2Measurement precision
If manual expert analysis is used to generate signatures, then high detection accuracy can be achieved, but the process is time-consuming and inefficient
Solution Approach 1:
The patent enables the system to automatically extract and analyze developer fingerprints from debug symbols without requiring manual expert intervention. The automated process parses debug information, identifies unique developer identifiers, and generates signatures independently, achieving both high detection accuracy and efficiency by eliminating time-consuming manual analysis while maintaining the precision of expert-level detection.
3Adaptability or versatility
If numerous known malware samples are collected for signature generation, then comprehensive detection coverage can be achieved, but the process becomes inefficient and resource-intensive
Solution Approach 1:
The patent extracts critical developer fingerprint information from a minimal set of malware samples. By focusing on extracting unique developer identifiers from debug symbols rather than analyzing entire malware behaviors across numerous samples, the system achieves comprehensive detection coverage with limited input data, resolving the contradiction between detection versatility and generation efficiency.
4Reliability
If traditional signature methods are used, then existing malware families can be detected, but new malware variants produce high false positives
Solution Approach 1:
The patent focuses on extracting specific local characteristics from malware - namely developer fingerprint information from debug symbols - rather than relying on general malware behavior patterns. These localized developer-specific identifiers provide reliable detection for both existing and new malware variants while minimizing false positives, as they directly trace malware to its creator rather than relying on generic detection patterns.
Data Source
AI summary
Techniques for generating malware signatures based on developer fingerprints in debug information are disclosed. In some embodiments, a system, process, and/or computer program product for generating malware signatures based on developer fingerprints in debug information includes receiving a sample, in which the sample includes a binary executable file; matching one or more paths in content of the binary executable file based on a plurality of patterns; extracting meta information from the one or more matched paths; and automatically generating a signature based on the extracted meta information.