Natural Language Interface for Cybersecurity Command Translation
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Solution Overview
Problem
Complex cybersecurity management systems are difficult for users to navigate and manipulate due to their intricate configurations and requirement for technical language, leading to a steep learning curve and potential for user errors, especially when interacting with natural language processing systems that are not intuitive and user-friendly.
Innovation Solution
A natural language interface system that uses machine learning models to convert natural language phrases into complex system commands, allowing users to interact with cybersecurity management systems in a more intuitive and efficient manner by receiving training data, augmenting it based on user inputs, and generating finalized commands for malware detection and mitigation tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cybersecurity management systems use complex configurations and technical language to ensure precise malware detection and system control, then the system's reliability and detection precision improve, but the ease of operation deteriorates due to steep learning curves and user errors
Solution Approach 1:
The patent introduces a natural language processing intermediary layer that translates user-friendly natural language commands into complex system commands. This mediator allows users to interact with the system using simple language while the underlying complex configurations and technical processes remain hidden, thus maintaining reliability without sacrificing ease of operation
Solution Approach 2:
The system segments the complex interaction process into two distinct layers: a user interface layer that handles natural language input and a system command layer that executes complex technical operations. This segmentation allows each layer to be optimized independently - the user interface for simplicity and the command layer for precision and reliability
2Manufacturing precision
If the system requires users to learn complex technical language and configurations to operate cybersecurity management systems effectively, then the precision of command execution improves, but the time required for operation increases due to learning curves
Solution Approach 1:
The patent replaces the mechanical system of learning and memorizing complex technical commands with an intelligent system using natural language processing and machine learning models. Users no longer need to invest time in learning technical language, as the system automatically interprets natural language intent and translates it into precise system commands, thereby maintaining execution precision while eliminating the time loss associated with learning curves
3Productivity
If machine learning models use limited training data to reduce training time and computational resources, then the training efficiency improves, but the model performance deteriorates due to insufficient learning capacity
Solution Approach 1:
The system performs preliminary actions by collecting and curating high-quality training data in advance, including diverse cybersecurity scenarios, commands, and contexts. This preliminary data preparation ensures that the model receives comprehensive training examples before deployment, enabling it to achieve high inference reliability even with efficient training processes that don't rely on brute-force data volume
4Measurement precision
If the system processes and analyzes extensive training data to improve model accuracy, then the model performance improves, but the computational resources and training time increase
Solution Approach 1:
The system performs preliminary processing and filtering of training data to identify and retain only the most relevant and informative examples. By pre-curating high-quality data with proper labeling and contextual information, the system achieves high inference precision using a more manageable dataset, thereby reducing the computational energy required for training while maintaining or improving model accuracy
Data Source
AI summary
In some embodiments, a processor receives natural language data for performing an identified cybersecurity task. The processor can provide the natural language data to a first machine learning (ML) model. The first ML model can automatically infer a template query based on the natural language data. The processor can receive user input indicating a finalized query and to provide the finalized query as input to a system configured to perform the identified computational task. The processor can provide the finalized query as a reference phrase to a second ML model, the second ML model configured to generate a set of natural language phrases similar to the reference phrase. The processor can generate supplemental training data using the set of natural language phrases similar to the reference phrase to augment training data used to improve performance of the first ML model and/or the second ML model.