Malware Detection via Statistical Pattern Optimization
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Solution Overview
Problem
Current signature-based malware detection systems are ineffective against zero-day attacks and generate high false positives due to their reliance on known threat signatures, making them prone to missing new malware and requiring extensive database updates.
Innovation Solution
An automated malware learning and detection system that generates and optimizes suspicious patterns using a statistical filter to reduce false positives, allowing for the detection of unknown malware by creating a revised set of patterns with a lower false positive rate and applying these patterns to incoming executables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If signature-based detection methods are used to detect malware, then detection accuracy for known threats is improved, but the system becomes vulnerable to zero-day attacks and cannot detect new malware
Solution Approach 1:
The system performs preliminary actions by proactively generating suspicious patterns from clean executables before malware can be created. These patterns are prepared in advance and stored for immediate use in detecting new malware variants, enabling the system to respond to zero-day threats without waiting for signature updates
Solution Approach 2:
Instead of deriving detection patterns from malware samples (traditional approach), the system inverts the process by generating suspicious patterns from clean, legitimate executables. This inversion allows the system to identify what normal behavior looks like and flag deviations, thereby detecting new malware without requiring prior knowledge of its characteristics
2Adaptability or versatility
If generic signatures are created to cover multiple malware variants, then detection coverage is improved, but false positive rate increases
Solution Approach 1:
The system applies local quality by creating highly specific suspicious patterns that are tailored to particular malware behaviors rather than using broad generic signatures. Each pattern is locally optimized to match specific malicious characteristics while avoiding false matches with legitimate software, thereby maintaining high detection coverage with low false positive rates
Solution Approach 2:
The system changes parameters by adjusting the specificity and granularity of detection patterns. Instead of using fixed generic signatures, the system dynamically generates patterns with optimized parameters that balance coverage and accuracy, allowing it to adapt to different malware types while minimizing false positives through precise parameter tuning
3Adaptability or versatility
If signature database is continuously updated with new malware signatures, then detection capability is improved, but system complexity and processing time increase
Solution Approach 1:
The system extracts only the essential suspicious patterns from clean executables that are most indicative of malware behavior, rather than storing complete signatures or comprehensive behavioral profiles. This extraction approach reduces database size while maintaining detection capability by focusing on the most discriminative features
Solution Approach 2:
The system uses disposable, lightweight suspicious patterns that can be rapidly generated and discarded rather than maintaining permanent, complex signature databases. These patterns are created on-demand from clean executables and can be quickly updated or replaced without significant overhead, reducing system complexity while maintaining adaptability
Data Source
AI summary
Methods and systems are described for malware learning and detection. According to one embodiment, an antivirus (AV) engine includes a training mode for internal lab use, for example, and a detection mode for use in commercial deployments. In training mode, an original set of suspicious patterns is generated by scanning malware samples. A set of clean patterns is generated by scanning clean samples. A revised set of suspicious patterns is created by removing the clean patterns from the original set. A further revised set of suspicious patterns is created by: (i) applying a statistical filter to the first revised set; and (ii) removing any suspicious patterns therefrom that do not meet a predefined frequency of occurrence. A detection model, based on the further revised set, can then be used in detection mode to flag executables as malware when the presence of one or more of the suspicious patterns is identified.