Binary Decision Tree Malware Signature Generation
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Solution Overview
Problem
Existing malware detection methods rely on predefined signatures that often fail to identify valid malware, due to their heuristic approaches, leading to incomplete detection capabilities.
Innovation Solution
A method involving training a binary decision tree using classified samples of executable software to derive malware-associated software feature signatures, which are then used by a computer-based security tool to identify and respond to malware, ensuring a predefined minimum detection rate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If heuristic approaches are used to generate malware signatures, then the detection process is simple, but the true positive rate is low and valid malware signatures are undiscovered
Solution Approach 1:
The patent replaces heuristic-based manual signature generation with an automated machine learning system. The ML model automatically analyzes malware samples, extracts features, and generates signatures without relying on human expertise or manual heuristic rules, thereby improving detection accuracy while maintaining operational simplicity.
Solution Approach 2:
The system enables self-service by allowing the malware detection system to automatically generate its own signatures through the ML pipeline. The system autonomously trains on malware samples, identifies patterns, and produces signatures without requiring external intervention or manual rule creation, improving both reliability and ease of operation.
2Productivity
If predefined malware signatures are used, then the detection process is fast, but the detection capability is incomplete and fails to identify valid malware
Solution Approach 1:
The system performs preliminary action by pre-training the ML model on a comprehensive dataset of malware samples before deployment. This offline training phase extracts and stores learned signatures that can be quickly applied during runtime detection, maintaining fast detection speed while improving capability through advance pattern recognition.
Solution Approach 2:
The patent changes the parameters of signature detection by transitioning from static predefined signatures to dynamic ML-generated signatures. The system adjusts detection parameters based on learned patterns from malware samples, enabling adaptive detection that maintains speed while improving accuracy through data-driven parameter optimization.
3Measurement precision
If more comprehensive malware analysis is performed, then the detection accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The system extracts only the most discriminative features and patterns from comprehensive malware analysis during the training phase. The ML model identifies and extracts key signature patterns that are sufficient for accurate detection, eliminating the need to process all possible features during runtime, thereby maintaining accuracy while reducing processing time.
Solution Approach 2:
The patent applies partial action by performing comprehensive analysis only during the offline training phase to generate signatures, while using only the essential extracted signatures during fast runtime detection. This selective application of analysis depth maintains high accuracy where needed while minimizing processing time during operational detection.
Data Source
AI summary
Deriving malware signatures by training a binary decision tree using known malware and benign software samples, each tree node representing a different software feature set and having one descending edge representing samples that are characterized by the node's software feature set and another descending edge representing samples that are not characterized thusly, selecting multiple continuous descending paths for multiple subsets of nodes, each path traversing a selected one of the edges descending from each of the nodes in its corresponding subset, deriving, based on the nodes and edges in any of the paths, a malware-associated software feature signature where the malware samples represented by leaves that directly or indirectly descend from an end of the continuous descending path meets a minimum percentage of the total number of samples represented by the leaves, and providing the malware signatures for use by a computer-based security tool configured to identify malware.