Behavioral Malware Signatures Using Generative AI Snapshots
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Solution Overview
Problem
Polymorphic malware evades detection by changing its code and behavior over time, challenging traditional anti-malware measures that rely on static signatures.
Innovation Solution
A framework using generative AI to capture and generate behavioral signatures of polymorphic malware by analyzing behavioral snapshots, generating prompts, and employing a generative model to produce an augmented signature.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional anti-malware measures use static signatures to detect malware, then detection accuracy is maintained, but polymorphic malware can evade detection by changing its code and behavior over time
Solution Approach 1:
The patent transitions from static signature-based detection to dynamic behavior-based detection. The system captures behavioral snapshots of malware execution and uses generative AI to generate signatures that adapt to the malware's changing behavior patterns, enabling reliable detection of polymorphic malware that constantly modifies its code.
Solution Approach 2:
The system changes the detection parameters from fixed static signatures to dynamic behavioral parameters. By monitoring multiple behavioral attributes (system calls, network traffic, file operations) and using generative AI to create signatures based on these changing parameters, the system can identify polymorphic malware despite its code transformations.
2Reliability
If the system captures multiple behavioral snapshots and uses generative AI to generate signatures, then detection capability against polymorphic malware improves, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary layer using generative AI models that bridge the gap between raw behavioral snapshots and detection signatures. This intermediary component automatically processes complex behavioral data and transforms it into actionable signatures, reducing the operational complexity of analyzing polymorphic malware behaviors.
Solution Approach 2:
The system employs self-service mechanisms where the generative AI model automatically generates detection signatures based on captured behavioral snapshots without requiring manual analysis. The system self-updates its detection capabilities by continuously learning from new malware behaviors, reducing the need for complex manual signature creation processes.
Data Source
AI summary
In one implementation, a device captures behavioral snapshots of a piece of executable code. The device generates a prompt based on the behavioral snapshots. The device generates a malware signature for the piece of executable code in part by inputting the prompt into a generative model. The device provides the malware signature for use to detect malware.