Malware Behavior Analysis Using Trace-BERT and Knowledge Graphs
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Solution Overview
Problem
Existing cybersecurity solutions, such as signature-based antivirus and anti-spyware, are inadequate for detecting and classifying novel and evolving malicious software, including ransomware and malware, due to their inability to adapt to rapidly changing threats.
Innovation Solution
A hybrid neuro-symbolic approach using large-scale transformer models like BERT, knowledge graphs, and symbolic methods to analyze dynamic program traces, construct a knowledge graph, and identify malicious actions, enabling classification and explanation of novel malware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If signature-based antivirus and anti-spyware protection measures are used, then existing security products can provide protection, but they are not sufficient to deal with constantly evolving cyberthreats including novel malware
Solution Approach 1:
The patent replaces traditional mechanical signature-based detection with a neural network-based system that uses deep learning models to analyze malware behavior patterns. The neural network processes dynamic execution traces and contextual information to identify malicious software, enabling adaptive detection that evolves with new threats rather than relying on static signatures.
Solution Approach 2:
The system changes the detection parameters from fixed signatures to dynamic behavioral patterns. By analyzing execution traces, system calls, and contextual data in real-time, the neural network adapts its detection criteria based on the specific behavior patterns of malware samples, allowing it to identify novel threats that don't match existing signatures.
2Adaptability or versatility
If neural network models are used to detect novel malware, then adaptability to evolving threats improves, but device complexity increases
Solution Approach 1:
The patent segments the complex detection system into distinct functional modules: a neural network component for pattern recognition, a knowledge graph component for contextual reasoning, and a trace collection component for data gathering. This segmentation allows each component to be optimized independently and simplifies the overall system architecture while maintaining high detection capability.
Solution Approach 2:
The patent introduces a knowledge graph as an intermediary between raw execution traces and final malware classification. The knowledge graph stores and organizes contextual information about malware behavior, serving as a mediator that translates complex neural network outputs into actionable classifications and explanations, thereby reducing the complexity burden on the main detection system.
3Measurement precision
If hybrid neuro-symbolic approach with transformer models and knowledge graphs is used, then classification accuracy and explanation capability improve, but manufacturing complexity increases
Solution Approach 1:
The patent designs the neural network model to serve multiple functions simultaneously: detecting malware presence, classifying malware types, and generating human-readable explanations. This multi-functionality reduces the need for separate specialized components, simplifying the overall system implementation while maintaining high classification accuracy and explainability.
Data Source
AI summary
Disclosed herein are devices, systems, and methods for detecting, understanding, and classifying malicious actions and/or behaviors in software (e.g., malware), including hidden malicious actions. Specifically, disclosed embodiments use natural language approaches to understand malicious software and provide explanations for classification results. At least one embodiment constructs a knowledge graph that includes textual explanations from source materials (e.g., articles), collecting one or more sets of dynamic program traces from one or more instances of malware, and constructing and training a model (also referred to herein as Trace-BERT) using the one or more sets of dynamic program traces. Forced execution of sample segments of computer code can also be used to identify hidden or novel malicious actions.