Malware Detection Using MinHash LSH Clustering
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Solution Overview
Problem
Current malware detection methods are inefficient in handling polymorphic malware variants and zero-day attacks, as they rely on signature-based approaches that are slow to update and struggle with scalability, leading to prolonged vulnerability in computer systems despite best cybersecurity practices.
Innovation Solution
The use of MinHash Locality Sensitive Hashing (LSH) for probabilistic clustering with sublinear complexity, combined with hierarchical clustering and partial learning, allows for rapid detection and classification of malware by focusing on static and dynamic file attributes, reducing the need for costly computations and enabling real-time learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If classical clustering algorithms (DBSCAN, Hierarchical) are used for malware classification, then clustering accuracy is maintained, but computational complexity increases to O(n2) or O(n3)
Solution Approach 1:
The patent divides the clustering process into two segments: (1) MinHash LSH pre-processing that groups similar malware samples into candidate clusters with sublinear complexity, and (2) exact clustering algorithm that performs precise clustering only on these pre-grouped candidates. This segmentation reduces the overall computational complexity from O(n2) or O(n3) to approximately O(n log n) or better, while maintaining clustering accuracy on the final result.
Solution Approach 2:
The patent applies MinHash LSH as a preliminary action before executing the exact clustering algorithm. This pre-processing step computes MinHash signatures and uses LSH to quickly identify and group similar malware samples into candidate clusters, filtering out dissimilar samples beforehand. This preliminary grouping significantly reduces the input size for the subsequent exact clustering step, thereby reducing computational complexity while preserving the ability to achieve accurate clustering results.
2Measurement precision
If signature-based malware detection is used, then detection accuracy for known malware is improved, but response time increases due to slow update cycles
Solution Approach 1:
The patent replaces the mechanical signature-based detection system with a clustering-based automated detection system. Instead of relying on manually created and updated signatures that require human analysis and distribution cycles, the system uses unsupervised clustering algorithms to automatically detect and classify new malware variants based on their intrinsic features. This substitution enables near-real-time detection without the time delays associated with manual signature creation and distribution.
Solution Approach 2:
The clustering-based system is self-service in that it automatically detects, clusters, and classifies new malware samples without requiring human intervention for signature creation. The system autonomously processes incoming malware samples, computes their features, performs clustering analysis, and generates classifications automatically. This self-service capability eliminates the time loss associated with manual update cycles while maintaining high detection accuracy.
3Adaptability or versatility
If polymorphic malware variants are analyzed using traditional methods, then comprehensive detection coverage is achieved, but analysis time increases significantly
Solution Approach 1:
The patent transforms the malware analysis problem by changing the parameters used for comparison. Instead of analyzing the complete binary code of polymorphic variants (which is time-consuming and complex), the system extracts and compares specific feature parameters such as opcode sequences, byte patterns, and structural characteristics. This parameter transformation enables rapid comparison of polymorphic variants while maintaining detection coverage, as the essential identifying features are preserved in the extracted parameters.
Solution Approach 2:
The patent segments the malware analysis process into feature extraction and clustering classification stages. During feature extraction, only relevant characteristics (opcodes, byte patterns, structural features) are extracted from each malware sample, ignoring irrelevant polymorphic variations. These extracted features are then used in the clustering stage to efficiently group and classify variants. This segmentation enables comprehensive detection coverage across polymorphic families while maintaining high analysis speed by focusing computational resources on discriminative features rather than entire binary contents.
Data Source
AI summary
A system and method for detecting malware using hierarchical clustering analysis. Unknown files classified by clustering and in view of known malicious and known safe files. Machine learning models and detection rules are used to enhance classification accuracy.