Command Line Anomaly Detection Using Transformer Embeddings
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Solution Overview
Problem
Existing systems struggle to effectively detect anomalous command line entries, which can indicate malware or software misconfigurations, due to the difficulty in developing rules or models that can identify multiple types of anomalies, especially as new anomalies emerge, and processing large volumes of entries.
Innovation Solution
An unsupervised machine learning-based system using a transformer model and anomaly detection model to analyze command line entries, generating anomaly scores by determining semantic and contextual meanings through command line embeddings, with a pre-processing stage to handle variable substrings and a cache for efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If rule-based or traditional detection models are used to identify anomalous command line entries, then the system can detect known anomaly patterns, but it struggles to identify multiple types of anomalies including new types that emerge over time
Solution Approach 1:
The patent replaces traditional rule-based mechanical detection systems with unsupervised machine learning models that automatically learn anomaly patterns from data. The transformer-based model substitutes manual rule creation with automated pattern recognition, enabling detection of both known and emerging anomaly types without requiring explicit programming of detection logic for each anomaly type.
Solution Approach 2:
The system changes the detection approach from fixed rule parameters to dynamic learned parameters through machine learning. The model learns optimal detection parameters automatically from training data, allowing it to adapt to new anomaly types by adjusting its internal parameters rather than requiring manual rule updates.
2Measurement precision
If comprehensive analysis of all command line entries is performed to ensure accurate anomaly detection, then detection accuracy is improved, but processing large volumes of entries becomes computationally intensive and time-consuming
Solution Approach 1:
The patent segments the command line entry analysis into distinct processing stages: preprocessing/tokenization, transformer embedding generation, and anomaly scoring. This segmentation allows parallel processing of different entries and optimization of each stage independently, improving overall processing throughput while maintaining detection accuracy.
Solution Approach 2:
The system performs preliminary preprocessing and tokenization of command line entries before main anomaly detection processing. By preparing data in advance and converting it to standardized embeddings, the system reduces the computational burden during the actual anomaly detection phase, enabling faster processing of large volumes of entries.
3Stability of the object's composition
If variable substrings in command line entries are handled through extensive pre-processing rules, then consistency in analysis is improved, but the complexity of the pre-processing stage increases
Solution Approach 1:
The patent replaces complex mechanical pre-processing rules with a transformer-based linguistic model that automatically handles variable substrings. The model learns to tokenize and normalize command line entries through training, substituting manual rule-based preprocessing with automated neural network-based processing that achieves consistency without requiring explicit rules for each variable pattern.
Data Source
AI summary
A command line anomaly detection system can generate anomaly scores associated with command line entries, such that command line entries associated with the highest anomaly scores can be identified. The command line anomaly detection system can include a transformer model trained, via unsupervised machine learning, to determine meanings of components of individual command line entries. The command line anomaly detection system can also include an anomaly detection model trained, via unsupervised machine learning, to determine anomaly scores based on the meanings of components of individual command line entries determined by the transformer model.