Malware Detection via Standardized Opcode N-gram Clustering
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Solution Overview
Problem
Conventional malware detection techniques, such as signature string scanning, are becoming less effective due to modern malware's targeted delivery and polymorphic nature, making it difficult to develop reliable detection signatures.
Innovation Solution
A method that extracts machine language instruction sequences from computer files, encodes them into standardized opcode sequences of uniform length, generates features based on these sequences, and classifies files as malware by analyzing these features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If signature string scanning is used for malware detection, then detection can be performed with simple methods, but detection reliability deteriorates due to polymorphic malware and targeted delivery
Solution Approach 1:
The patent transforms the detection approach by changing parameters from signature-based pattern matching to opcode sequence analysis. It extracts and encodes opcodes into standardized sequences, then uses N-gram features and clustering algorithms to detect malware based on behavioral patterns rather than static signatures, thereby improving reliability against polymorphic threats
Solution Approach 2:
The patent replaces the mechanical signature scanning system with an automated analysis system that extracts machine code instructions, encodes them into standardized opcodes, generates statistical features, and applies clustering algorithms. This substitution enables dynamic adaptation to new malware variants without manual signature updates
2Measurement precision
If signature strings are developed for each malware instance, then detection accuracy improves, but device complexity and time consumption increase significantly
Solution Approach 1:
The patent creates a universal detection framework that processes diverse malware types through a common pipeline: opcode extraction, standardization, N-gram feature generation, and clustering. This multi-functional system handles targeted, polymorphic, and mass-distributed malware uniformly, reducing the need for instance-specific detection mechanisms
Solution Approach 2:
The patent segments the malware detection problem into distinct processing stages: instruction extraction, opcode encoding, feature generation, and classification. It also segments malware analysis into N-gram sequences that capture local instruction patterns, enabling precise detection without requiring complete system complexity
3Adaptability or versatility
If signature scanning is used for mass-distributed malware, then broad coverage is achieved, but polymorphic variants evade detection
Solution Approach 1:
The patent introduces dynamic analysis through opcode sequence extraction and N-gram feature generation that adapts to different malware behaviors. The clustering algorithm dynamically groups similar malware instances, allowing the system to automatically adjust to new polymorphic variants while maintaining broad coverage across different malware types
Data Source
AI summary
Machine language instruction sequences of computer files are extracted and encoded into standardized opcode sequences. The standardized opcodes in the sequences are of the same length and do not include operands. A multi-dimension vector is generated as a static feature for each computer file, where each element in the vector corresponds to the number of occurrences of a unique N-gram (i.e., unique sequence of N consecutive standardized opcodes) in the standardized opcode sequence for that computer file. The computer files are clustered into clusters of similarly classified files based on similarities of their static features. An unknown computer file can be classified by first grouping the file into a cluster of files with similar static features (e.g., into the cluster with the shortest average distance), and then determining the classification of that file based on the classifications of other files that belong to the same cluster.