Malware Classification Using Contextual Cognition Engine
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Solution Overview
Problem
Existing malware classification techniques struggle to accurately classify new or uncommon malware due to limitations in available knowledge, leading to difficulties in identifying zero-day threats and impeding manual classification processes.
Innovation Solution
A system and method that includes a cognition engine to generate and collect contextual information associated with files, enabling manual classification and updating the malware classifier to improve its ability to classify similar files in the future.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If signature-based detection is used to classify malware, then classification accuracy for known malware is improved, but the ability to detect new and uncommon malware deteriorates
Solution Approach 1:
The system performs preliminary analysis by extracting multiple features from files (static features, dynamic behavior, sandbox execution results) before classification. This preliminary action creates a comprehensive feature set that enables the classifier to handle both known and new malware types effectively
Solution Approach 2:
The system changes the classification parameters by using multiple feature types (file headers, behavioral characteristics, sandbox results) instead of relying on a single parameter type. This multi-parameter approach allows the classifier to adapt to different malware types while maintaining accuracy for known threats
2Adaptability or versatility
If anomaly-based detection is used to identify malware, then detection of new malware is improved, but false positive rate increases
Solution Approach 1:
The system implements feedback mechanisms where classification results and analyst corrections are used to continuously refine the classifier. This feedback loop reduces false positives by learning from actual classification outcomes and adjusting detection thresholds and feature weights accordingly
Solution Approach 2:
The system introduces an intermediary human analyst layer that reviews borderline cases and provides ground truth labels. This intermediary resolves ambiguous anomaly detections, reducing false positives while maintaining high detection capability for new malware
3Measurement precision
If manual classification is performed for unclassified files, then classification accuracy is improved, but processing time increases
Solution Approach 1:
The system applies partial automation by using the classifier for initial assessment and only escalating files that exceed a confidence threshold to manual review. This partial action approach maintains high accuracy for confident classifications while reducing processing time by automating the majority of cases
Solution Approach 2:
The system performs preliminary automated classification and feature extraction before manual review is needed. This preliminary action prepares files for manual classification in advance, reducing the actual manual processing time while maintaining accuracy
4Adaptability or versatility
If the malware classifier knowledge base is expanded to detect more malware types, then detection capability is improved, but system complexity increases
Solution Approach 1:
The system segments the classification task into multiple independent feature extraction modules (static analysis, dynamic analysis, sandbox evaluation) and a separate classification engine. This segmentation allows the knowledge base to expand without increasing overall system complexity, as each module remains independently manageable
Data Source
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AI summary
Contextual information associated with a file is provided to at least enable a classification of the file when a malware classifier is unable to classify the file. In response to the providing of the contextual information, the classification of the file is received. Based at least on the received classification of the file, the malware classifier is updated to enable the malware classifier to classify the file.