Transfer Learning for Cross-Domain Cybersecurity Threat Detection
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Solution Overview
Problem
Cybersecurity solutions struggle with classifying emergent threats due to a lack of historical data, leading to vulnerabilities from new attack vectors, and existing machine learning models are susceptible to false positives and negatives without sufficient representative samples.
Innovation Solution
Transfer learning is employed to leverage a fully-trained machine learning model from a different domain with disparate representations to train a model for a domain with insufficient data, using trainable layers and adjusting weights to enhance cybersecurity threat detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional machine learning models are trained from scratch for a new domain, then the model can be optimized for that specific domain, but the training process requires large amounts of historical data which are unavailable for emergent threats
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on source domains with abundant historical cybersecurity data before deploying them to target domains with limited data. The models are trained in advance on well-studied attack vectors, and then these pre-trained models are transferred to detect emergent threats where historical data is scarce, eliminating the need to start training from scratch
Solution Approach 2:
The patent uses transfer learning as an intermediary mechanism that bridges source domains with rich historical data and target domains with limited data. The pre-trained models from source domains serve as intermediaries, transferring their learned representations and features to the target domain, enabling effective threat detection without requiring extensive target domain training data
2Measurement precision
If domain-specific machine learning models are trained separately for each cybersecurity domain, then the models can capture domain-specific characteristics, but the development time and computational resources required increase significantly
Solution Approach 1:
The patent implements universality by creating machine learning models with multi-functionality that can operate across multiple cybersecurity domains. A single pre-trained model serves multiple target domains by transferring its learned capabilities, eliminating the need to develop separate models for each domain while maintaining domain-specific detection precision through adaptive fine-tuning
Solution Approach 2:
The patent applies parameter changes by adjusting model parameters during transfer learning to adapt pre-trained models to target domains. The models undergo parameter optimization when fine-tuned on target domain data, allowing them to capture domain-specific characteristics while leveraging knowledge from source domains, thus reducing training time compared to training from scratch
3Productivity
If machine learning models are trained on limited data for emergent threats, then the training process is faster and more resource-efficient, but the models suffer from false positives and negatives due to insufficient representative samples
Solution Approach 1:
The patent applies preliminary action by pre-training models on source domains with abundant data before deploying them to target domains with limited data. This pre-training ensures that models learn robust feature representations and attack patterns from well-studied domains, providing a strong foundation that reduces false positives and negatives even when target domain data is limited
Solution Approach 2:
The patent uses transfer learning as an intermediary that transfers knowledge from source domains to target domains. This knowledge transfer acts as a mediator that compensates for the limitations of small target domain datasets, enabling models to achieve higher classification accuracy by leveraging patterns and features learned from source domains with rich historical data
Data Source
AI summary
A method includes training a first machine learning model with a first dataset, to produce a first trained machine learning model to infer cybersecurity-oriented file properties and/or detect cybersecurity threats within a first domain. The first dataset includes labeled files associated with the first domain. The first trained machine learning model includes multiple layers, some of which are trainable. A second trained machine learning model is generated, via a transfer learning process, using (1) at least one trainable layer from the multiple trainable layers of the first trained machine learning model, and (2) a second dataset different from the first dataset. The second dataset includes labeled files associated with a second domain. The first domain has a different syntax, different semantics, and/or a different structure than that of the second domain. The second trained machine learning model (e.g., a deep neural network model) is then available for use in inferring cybersecurity-oriented properties of the file in the second domain and/or detecting cybersecurity threats in the second domain.