Agglomerative Clustering for Unknown Malware Classification
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Solution Overview
Problem
Current malware classification methods are time-intensive and prone to bias, relying heavily on manual analysis and signature-based detection, which struggles with unknown malware samples and obfuscation techniques, leading to backlogs and inaccuracies in identifying malicious software.
Innovation Solution
The use of agglomerative clustering to generate a dendrogram for unknown malware samples, allowing security researchers to classify them as variants of known families, new families, or benign samples by determining their proximity to anchor points, with a confidence score to automate the classification process and continuously improve accuracy over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis and signature-based detection are used for malware classification, then detection accuracy for known malware is improved, but analysis time and workload increase significantly
Solution Approach 1:
The system performs preliminary automated clustering analysis on malware samples to generate dendrograms and identify potential malware families before manual analysis. This preliminary action groups similar samples together, allowing researchers to focus on uncertain cases while routine samples are pre-classified, thereby reducing overall analysis time while maintaining accuracy.
Solution Approach 2:
The patent introduces an automated clustering system as an intermediary between signature-based detection and manual analysis. This intermediary processes samples through agglomerative clustering to generate dendrograms, which then guide subsequent manual or automated classification, reducing the direct burden on manual analysis while preserving detection accuracy.
2Measurement precision
If manual analysis is used for unknown malware samples, then classification accuracy is improved, but researcher bias and workload increase
Solution Approach 1:
The system enables self-service classification by allowing the clustering algorithm to automatically analyze and group unknown malware samples based on their features. The dendrogram generation and anchor point identification occur without researcher intervention, objectively classifying samples while reducing bias and workload. Researchers only need to review and validate results rather than perform complete manual analysis.
Solution Approach 2:
The patent replaces the mechanical process of manual manual analysis with an automated computational clustering system. The agglomerative clustering algorithm mechanically processes samples through defined mathematical operations, eliminating human bias and reducing workload while maintaining consistent classification accuracy across all samples.
3Speed
If signature-based detection is used for malware identification, then detection speed for known malware is improved, but effectiveness against unknown malware and obfuscation decreases
Solution Approach 1:
The system transitions from static signature-based detection to dynamic feature-based clustering. Instead of relying on fixed signatures, the system dynamically extracts features from malware samples and adapts to new patterns through clustering analysis. This allows the system to maintain detection speed while becoming effective against unknown malware and obfuscated variants by identifying structural similarities rather than fixed signatures.
Solution Approach 2:
The patent changes the detection parameters from fixed signature strings to dynamic feature vectors that capture malware behavior and structure. By transforming samples into feature spaces and using clustering algorithms, the system can detect unknown malware based on parameter similarities even when signatures differ, thereby improving adaptability while maintaining detection speed through automated processing.
4Productivity
If automated clustering is used for malware classification, then processing speed and scalability are improved, but system complexity increases
Solution Approach 1:
The patent segments the malware classification system into distinct modular components: feature extraction module, clustering module, dendrogram generation module, and validation module. Each module performs a specific function with well-defined interfaces, making the overall complex system manageable and maintainable while enabling high processing speed through specialized optimization of each segment.
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture are disclosed for classification of unknown samples using agglomerative clustering. An apparatus includes an extractor to extract a feature from a sample source code, the feature including at least one of a register, a variable, or a library based on a threshold of occurrence in a corpus of samples, the corpus of samples including malware samples, a dendrogram generator to generate a dendrogram based on features extracted from the sample source code, the dendrogram representing a collection of samples clustered based on similarity among the samples, the samples including sample clusters belonging to known malware families, and an anchor point identifier to traverse the dendrogram to identify similarity of an unknown sample to the sample clusters based on a confidence score, and identify anchor point samples from the sample clusters identified as similar to the unknown sample, the anchor point samples to provide metadata for use in extrapolating information to classify the unknown sample.