LLM Malware Variant Generation for Polymorphic Threat Detection
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Solution Overview
Problem
Traditional malware detection methods, such as signature-based scanning, are ineffective against rapidly evolving threats that employ slight variations of known malware, leading to potential system compromise and damage.
Innovation Solution
Utilizing a Large Language Model (LLM) to generate polymorphic variants of malware, trained on behavioral characterizations of known malware, enabling detection of new variations by creating samples with similar behaviors but different code sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If signature-based scanning is used for malware detection, then detection of known malware is effective, but detection of newly released malware with variations is ineffective
Solution Approach 1:
The system performs preliminary action by generating polymorphic variants of known malware samples before actual detection occurs. The LLM creates multiple modified versions of malware that preserve harmful behavior while changing code sequences, allowing the detection system to pre-learning these variants and improve future detection of new malware families.
Solution Approach 2:
The system creates copies of known malware samples through polymorphic transformation. Instead of relying on exact signature matching, the LLM generates multiple copied versions with different code sequences that maintain the same behavioral characteristics, enabling detection of malware family patterns rather than exact matches.
2Reliability
If manual scanning is employed to prevent malware threats, then detection effectiveness improves, but time consumption and cost increase
Solution Approach 1:
The system replaces manual scanning mechanisms with an automated LLM-based polymorphic generation system. The LLM automatically creates malware variants and updates detection signatures without human intervention, maintaining high detection reliability while eliminating the time loss and cost associated with manual scanning operations.
Solution Approach 2:
The system implements self-service by enabling the detection system to automatically generate its own training data and update its signatures. The LLM autonomously creates polymorphic variants from known malware samples, allowing the system to continuously improve its detection capabilities without requiring manual analysis of new threats.
3Adaptability or versatility
If polymorphic variants are generated using LLM, then detection capability against new malware improves, but system complexity increases
Solution Approach 1:
The LLM serves as an intermediary between known malware samples and the detection system. Instead of directly analyzing complex malware variations, the LLM mediates by generating simplified polymorphic variants that capture essential behavioral patterns, making the detection system more adaptable without proportionally increasing its complexity.
Data Source
AI summary
Systems, methods, and computer-readable media are disclosed for detecting a malware sample by creating polymorphic variants of a malware sample using a large language model. The technology can obtain a known malware sample and decompose the known malware sample into behavioral characterizations of the known malware sample that correspond to respective processes taken by the known malware sample. The technology can then train a large language model with data corresponding to the behavioral characterizations and generate polymorphic variants of the known malware sample with a large language model based on the behavioral characterizations. When the technology later receives a potential malware sample, it can analyze the potential malware sample by comparing the potential malware sample to the polymorphic variants of the known malware sample generated by the large language model.