Automatic Network Signature Generation via RNN Analysis
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Solution Overview
Problem
Conventional malware signature generation techniques are time-consuming and resource-intensive, requiring manual effort and being insensitive to emerging campaigns, which can lead to false positives due to expired or low-quality signatures.
Innovation Solution
The development of a system that automatically generates network signatures by creating network profiles for malware samples, selecting signature candidates, evaluating them, and distributing new signatures to security devices to detect malware, utilizing machine learning models like recurrent neural networks (RNNs) for attention-based analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual malware signature generation techniques are used, then signature quality can be maintained, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The system enables automated self-generation of malware signatures through machine learning models that automatically analyze network traffic, identify malware patterns, and generate signatures without requiring continuous manual intervention. The RNN-based attention model processes network events autonomously to produce high-quality signatures.
Solution Approach 2:
The patent replaces manual mechanical signature generation processes with automated machine learning systems. The RNN-based attention model substitutes human analysts by automatically processing network traffic data, identifying malicious patterns, and generating signatures through computational algorithms rather than human effort.
2Reliability
If conventional signature generation methods are used, then existing malware can be detected, but emerging malware campaigns are not detected quickly enough leading to false positives
Solution Approach 1:
The system performs preliminary analysis of network traffic using the RNN-based attention model to identify emerging malware patterns before they can cause widespread false positives. By continuously monitoring and learning from network events, the system prepares signatures in advance for emerging threats.
Solution Approach 2:
The signature generation system is dynamic and adaptive, continuously updating its understanding of malware patterns through the RNN model's ability to process sequential network events. This dynamic approach allows the system to quickly adapt to emerging malware campaigns rather than relying on static signature sets.
3Measurement precision
If manual signature creation is performed, then signature precision can be ensured, but the complexity and resource requirements increase significantly
Solution Approach 1:
The patent replaces complex manual signature creation processes with an automated RNN-based machine learning system. The attention model handles the complexity of analyzing sequential network events and identifying precise malware patterns automatically, reducing the need for manual intervention while maintaining high signature precision.
Data Source
AI summary
Automatic generation of network signatures is disclosed. Network profiles for malware samples are generated. Network signature candidates are selected based on the network profiles. The network signature candidates are automatically evaluated to automatically generate a new set of network signatures. The new set of network signatures is distributed to a security device/service to enforce the new set of network signatures to detect malware.