Malware Embedding Network for Zero-Day Detection
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Solution Overview
Problem
Current state-of-the-art Deep Learning approaches for malware similarity analysis and zero-day family detection lack scalability for real-world malware triage environments and do not provide a mechanism for unknown family detection.
Innovation Solution
A multi-Machine Learning system is proposed, comprising an embedding network trained using triplet loss and reinforcement-based Expectation Maximization learning for generalization, combined with an open-set approximate nearest neighbor algorithm for family matching and zero-day detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning approaches are used for malware similarity analysis, then detection accuracy is improved, but scalability is worsened
Solution Approach 1:
The malware sample is divided into multiple byte string fragments of predetermined length. Each fragment is independently embedded and analyzed to generate individual predictions, which are then aggregated to determine the final malware family classification. This segmentation enables parallel processing of fragments, improving scalability while maintaining detection accuracy through collective analysis of multiple fragments.
2Ease of operation
If traditional signature-based methods are used, then ease of operation is improved, but adaptability to new malware is worsened
Solution Approach 1:
The system pre-trains the embedding network using triplet loss on known malware families to learn meaningful representations of malware characteristics. This preliminary training enables the system to detect zero-day malware by comparing new samples against the learned embeddings, providing adaptability to unknown malware while maintaining ease of operation through automated comparison processes.
Solution Approach 2:
The embedding network acts as an intermediary that transforms raw malware byte strings into meaningful vector representations. These embeddings serve as a bridge between traditional signature-based methods and deep learning approaches, enabling the system to handle both known and unknown malware families effectively.
3Measurement precision
If fragment-level analysis is performed, then measurement precision is improved, but device complexity is worsened
Solution Approach 1:
The system combines multiple fragment-level predictions through aggregation to produce a final malware family classification. By merging the results from individual fragment analyses, the system achieves high measurement precision through collective decision-making while managing device complexity through efficient aggregation algorithms that synthesize multiple inputs into a single output.
Data Source
AI summary
A method at a computing device including fragmenting a malware sample into a plurality of byte strings, each of the plurality of byte strings having a predetermined length; embedding each of the plurality of byte strings in an embedding network to generate a plurality of embeddings; for each embedding in the plurality of embeddings, finding a nearest neighbor; and setting a predicted family for the malware sample based on a fusion of the nearest neighbor for each of the plurality of embeddings.