Adaptive Malware Analysis Controller for Dynamic Threat Detection
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Solution Overview
Problem
Existing malware detection systems face inefficiencies and inflexibility due to the sequential and separate nature of static and dynamic analyses, which can miss polymorphic malware and result in delayed detection and high false positives, especially when dealing with advanced persistent threats.
Innovation Solution
A malware detection system that integrates intelligent static and dynamic analyses with a controller determining an analysis plan that can include both static and dynamic analyses in various orders and protocols, optionally incorporating emulation analysis for enhanced efficiency and accuracy, allowing for flexible and adaptive analysis protocols based on feedback and intelligence stored in a shared database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sequential static and dynamic analyses are performed separately, then detection coverage is improved, but detection time and false positives increase
Solution Approach 1:
The system dynamically adjusts the analysis protocol based on feedback from initial analyses. The controller determines whether to perform static analysis, dynamic analysis, or both in sequence based on the characteristics of the content being analyzed. This adaptive approach optimizes detection time while maintaining comprehensive coverage by avoiding unnecessary analyses.
Solution Approach 2:
The system implements feedback loops where results from static analysis inform the decision to proceed with dynamic analysis, and results from dynamic analysis are used to update detection models. This feedback mechanism improves detection accuracy over time and reduces false positives by learning from previous analyses.
2Measurement precision
If signature matching is used for malware detection, then known malware is detected accurately, but polymorphic malware is missed
Solution Approach 1:
The system merges signature-based detection with behavior-based detection. Static analysis examines file characteristics and signatures, while dynamic analysis observes actual execution behavior. By combining both approaches, the system maintains high accuracy for known malware while gaining the ability to detect polymorphic variants through behavioral patterns.
Solution Approach 2:
The system performs preliminary static analysis to identify suspicious characteristics before proceeding to dynamic analysis. This preliminary action allows the system to focus computational resources on potentially malicious files, improving both detection accuracy and efficiency while capturing polymorphic variants that would be missed by signature matching alone.
3Reliability
If dynamic analysis is performed on all suspicious content, then detection accuracy is improved, but system resource consumption increases
Solution Approach 1:
The system performs partial dynamic analysis only on content that exhibits suspicious characteristics during static analysis. Rather than analyzing all content dynamically, the system applies dynamic analysis selectively to a subset of suspicious files, maintaining high detection accuracy while significantly reducing resource consumption compared to universal dynamic analysis.
Solution Approach 2:
The system dynamically determines the analysis protocol for each piece of content based on characteristics identified during static analysis. Files showing strong indicators of malice proceed to dynamic analysis, while less suspicious files are handled through static analysis alone or rejected, optimizing resource allocation based on real-time assessment of risk.
4Reliability
If manual signature updates are performed, then detection of new malware is improved, but response time increases
Solution Approach 1:
The system implements automated feedback loops where dynamic analysis results are fed back into the detection models and signature databases. This continuous learning process allows the system to automatically adapt to new malware variants without manual intervention, maintaining high detection reliability for new threats while eliminating the time delay associated with manual signature updates.
Solution Approach 2:
The system performs self-updating through automated analysis of suspicious content and learning from detection results. The controller automatically adjusts detection parameters and updates internal models based on accumulated intelligence, enabling the system to detect new malware variants without requiring manual signature creation and distribution, thus reducing response time significantly.
Data Source
AI summary
A method of detecting malware in a specimen of computer content or network traffic is described. The method features conducting a first analysis on the specimen in accordance with a first plurality of analyses and an order of the first plurality of analyses. A second analysis is conducted on the specimen different than the first analysis type. Thereafter, further analyses on the specimen may be altered by modifying information associated with the first plurality of analyses or the order of the first plurality of analyses in response to feedback information based on results from at least the first analysis. The modified information changes a malware analysis of the specimen from being conducted in accordance with the first plurality of analyses to being conducted in accordance with a second plurality of analyses different in analysis type or in order of analyses than the first plurality of analyses.