Malware Behavioral Signature Clustering
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Solution Overview
Problem
Existing malware detection methods face challenges in efficiently identifying malicious software due to the vast variety of malware and the complexity of maintaining behavioral signatures, which increases computational resources and complexity as the number of signatures grows.
Innovation Solution
A method that collects and normalizes behavior traces of malware, clusters similar sequences, and identifies common subsequences to create behavioral signatures for malware families, reducing the number of signatures needed and automating the detection process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the number of behavioral signatures is increased to detect more malware varieties, then the detection coverage is improved, but the computational resources and system complexity increase
Solution Approach 1:
The patent merges multiple individual malware behavior sequences into a single clustered behavioral signature that represents an entire malware family. By clustering similar behavior sequences together and creating one representative signature per cluster, the system detects multiple malware varieties without proportionally increasing the number of signatures, thus reducing computational overhead while maintaining detection coverage.
Solution Approach 2:
The patent creates universal behavioral signatures that apply to entire malware families rather than individual malware instances. Each clustered signature serves multiple detection purposes across different malware variants within the same family, making the signature system more versatile without increasing complexity.
2Adaptability or versatility
If the number of behavioral signatures is increased to detect more malware varieties, then the detection coverage is improved, but the computational resources required increase
Solution Approach 1:
The patent combines multiple behavior sequences into single clustered signatures, reducing the total number of signatures the system must process. This merging approach maintains the ability to detect diverse malware while significantly reducing computational resource consumption during detection operations.
3Measurement precision
If manual maintenance of behavioral signatures is performed to ensure accuracy, then the detection precision is improved, but the ease of operation deteriorates
Solution Approach 1:
The patent implements automated clustering algorithms that self-organize behavior sequences into meaningful groups without human intervention. The system automatically identifies patterns, creates clustered signatures, and updates the signature database autonomously, eliminating manual maintenance while preserving detection precision through algorithmic pattern recognition.
Solution Approach 2:
The patent replaces manual signature maintenance (mechanical human operation) with automated computational clustering algorithms. The system uses computational methods to analyze behavior sequences, identify clusters, and generate signatures automatically, substituting human effort with algorithmic processing while maintaining or improving precision.
Data Source
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AI summary
A behavioral signature for detecting malware is generated. A computer is used to collect behavior traces of malware in a malware dataset. The behavior traces describe sequential behaviors performed by the malware. The behavior traces are normalized to produce malware behavior sequences. Similar malware behavior sequences are clustered together. The malware behavior sequences in a cluster describe behaviors of a malware family. The cluster is analyzed to identify a behavior subsequence common to the cluster's malware family. A behavior signature for the malware family is generated using the behavior subsequence. A trace of new malware is normalized and aligned with an existing cluster, if possible. The behavioral signature for that cluster is generated based on the behavior sequence of the new malware and the other sequences in the cluster.