Malware Clustering via Static Analysis and Decision Trees
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Solution Overview
Problem
Current malware detection methods, such as phase 1 signatures and behavior-based or dynamic analysis, are ineffective in keeping pace with the rapid evolution of malware, as they require manual signature generation, are time-consuming, and struggle with sandbox evasion and malformed malware.
Innovation Solution
A machine-learning based approach using static analysis and automatic clustering of malware samples through a decision tree model, where feature vectors are extracted, converted into input vectors, and used to generate byte sequences for hash value calculation, allowing for classification and clustering of malware into known families or new variants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If phase 1 signatures are used to identify malware, then malware detection capability is provided, but the system becomes ineffective as signatures quickly explode and cannot identify unknown malware samples
Solution Approach 1:
The patent replaces the manual mechanical process of signature creation and maintenance with an automated machine learning system. The ML model automatically generates detection rules and clusters malware samples without requiring manual analyst intervention, thus substituting the mechanical signature management process with an automated intelligent system that scales efficiently
Solution Approach 2:
The system enables self-service by allowing the malware detection system to automatically learn from new malware samples and generate its own detection signatures through machine learning. The automated clustering and signature generation process eliminates the need for external manual intervention, making the system self-sustaining and adaptive to new threats
2Reliability
If manual signature generation is performed by experienced analysts, then general signatures can be created, but the process cannot keep up with the pace of new malware
Solution Approach 1:
The patent replaces the manual mechanical process of signature creation and maintenance with an automated machine learning system. The ML model automatically generates detection rules and clusters malware samples without requiring manual analyst intervention, thus substituting the mechanical signature management process with an automated intelligent system that scales efficiently
Solution Approach 2:
The system enables continuous automated signature generation and malware analysis without interruption. The machine learning model operates continuously to process new malware samples, generate signatures, and update detection rules, eliminating the discontinuous nature of manual analysis and maintaining constant protection against evolving threats
3Reliability
If behavior-based or dynamic analysis is used to identify malware, then malware detection is possible, but the process is time consuming and cannot handle large volumes of samples
Solution Approach 1:
The patent applies preliminary action by performing static analysis on malware samples before they are executed in sandbox environments. The machine learning model pre-processes and analyzes malware characteristics from static data, generating initial detection rules and clustering results that can be quickly evaluated, thus avoiding time-consuming dynamic analysis for all samples
Solution Approach 2:
The system segments the malware analysis process into multiple stages: static analysis using machine learning for rapid initial assessment, followed by selective dynamic analysis only for samples that require further investigation. This segmentation allows the system to process large volumes of samples efficiently by applying the more resource-intensive dynamic analysis only when necessary
4Reliability
If sandbox environments are used for dynamic analysis, then malware behavior can be observed, but sandbox evasion technology and inability to handle malformed malware reduce effectiveness
Solution Approach 1:
The patent applies preliminary action by performing static analysis on malware samples before they are executed in sandbox environments. The machine learning model pre-processes and analyzes malware characteristics from static data, generating initial detection rules and clustering results that can be quickly evaluated, thus avoiding time-consuming dynamic analysis for all samples
Solution Approach 2:
The machine learning model is designed with universal applicability to handle various types of malware including malformed and corrupted samples. By using static analysis features that can be extracted from any malware binary regardless of its execution state or evasion techniques, the system provides multi-functional detection capability that works across diverse malware types without requiring successful execution
Data Source
AI summary
Systems and methods for a machine learning based approach for identification of malware using static analysis and a machine-learning based automatic clustering of malware are provided. According to various embodiments of the present disclosure, a processing resource of a computer system receives a potential malware sample. A plurality of feature vectors is extracted from the potential malware sample and is converted into an input vector. A byte sequence is generated by walking a plurality of decision trees based on the input vector. Further, a hash value for the byte sequence is calculated and a determination is made regarding whether the hash value matches a malware hash value of a plurality of malware hash values corresponding to a known malware sample. Upon said determination being affirmative, the potential malware sample is classified as malware and is associated with a malware family of the known malware sample.