Malware Detection Rule Generation via Feature Matching
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Solution Overview
Problem
Existing malware detection systems face challenges in keeping malware strain-specific rulesets up to date, as new variants of malware strains emerge, leading to obsolete rules that require frequent updates to maintain effectiveness.
Innovation Solution
A method is introduced to systematically discover and update obsolete YARA rules by monitoring network communications, extracting features, and generating new rules based on non-matches and matches, which are then validated and merged with existing rules to create updated rule sets that can disable, block, or delete malicious files.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing YARA rules are used for malware detection, then current malware strains can be detected, but new malware variants appear causing rules to become obsolete and detection effectiveness to decrease
Solution Approach 1:
The system implements automated feedback loops where detection results from YARA rules are continuously monitored. When new malware variants are detected that evade existing rules, this information feeds back into the rule generation process, automatically creating updated rules that incorporate lessons learned from the evasion techniques observed.
Solution Approach 2:
The system performs self-updating of detection rules through automated analysis of malware samples and generation of new YARA rules. The rule generation module autonomously creates updated rules without requiring manual intervention, allowing the system to maintain and improve its own detection capabilities continuously.
2Adaptability or versatility
If multiple YARA rules are used to cover each malware strain, then detection versatility increases, but ruleset complexity and difficulty of maintenance increase
Solution Approach 1:
The system merges multiple detection rules by identifying common patterns and features across different malware strains. The rule generation module consolidates overlapping rules and creates unified detection logic, reducing the total number of rules while maintaining comprehensive coverage of malware variants.
Solution Approach 2:
The system creates universal detection rules that can identify multiple malware strains through common behavioral patterns and features. Rather than creating separate specialized rules for each strain, the generated rules are designed to be multi-functional, detecting various malware types that share similar characteristics.
3Measurement precision
If manual rule updates are performed to keep ruleset fit, then detection accuracy is maintained, but time consumption and operational effort increase
Solution Approach 1:
The system automatically maintains detection accuracy through self-updating mechanisms. The rule generation module continuously analyzes new malware samples and generates updated rules autonomously, eliminating the need for manual rule maintenance while preserving high detection accuracy through automated precision tuning.
Solution Approach 2:
The system performs preliminary analysis of malware samples and proactively generates updated rules before manual intervention would be needed. By continuously monitoring for new variants and pre-generating detection rules, the system maintains accuracy without requiring reactive manual updates.
Data Source
AI summary
A method and system for updating and applying a ruleset used for determining and mitigating malware threats. Communications of computing devices are monitored and first data file extracted. A first and second set of features are extracted. A first rule is applied to the first set of features of the first data file to determine a non-match. A second rule is applied to the second set of features to determine a match. A third rule is generated based on the first set of features, non-match, and match. Communications of a particular computing device are monitored and second data file extracted. A first set of features of the second data file are extracted. The third rule is applied to the first set of features of the second data file to determine a match. The second data file is disabled, blocked, or deleted based the match determination by the third rule.