ML Command Description Generation for Cybersecurity Analysis
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Solution Overview
Problem
Cybersecurity analysts often struggle to understand complex computing commands, leading to inaccurate flagging of events or missed detections, due to the complexity and difficulty in deciphering these commands.
Innovation Solution
A system that uses a machine learning model to generate multiple natural language descriptions of a command, compares these descriptions to the original command using a distance function, and selects the most similar description to display as an explanation of the command.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cybersecurity analysts manually analyze complex computing commands, then they can evaluate potential risks, but the complexity and difficulty in deciphering commands lead to inaccurate flagging or missed detections
Solution Approach 1:
The patent introduces an intermediary system comprising a machine learning model and a natural language processing system that acts as a mediator between the complex computing command and the cybersecurity analyst. The system generates multiple natural language descriptions of the command, compares them against a database of known commands, and presents the most accurate description to the analyst, thereby resolving the contradiction between command complexity and analysis accuracy
Solution Approach 2:
The patent replaces the manual mechanical analysis process with an automated electronic system using machine learning models and natural language processing. The system automatically generates descriptions, compares commands using distance functions, and identifies matches without requiring manual deciphering of complex command syntax, thus improving reliability while reducing the impact of command complexity
2Reliability
If cybersecurity analysts spend more time studying complex commands, then understanding improves, but time consumption increases and productivity decreases
Solution Approach 1:
The system performs preliminary actions by pre-generating multiple natural language descriptions of the command and pre-comparing them against the database of known commands before presenting results to the analyst. This preliminary processing eliminates the need for analysts to spend time manually studying command syntax, as the system has already performed the analytical work in advance
Solution Approach 2:
The system creates multiple copies or variations of natural language descriptions for each command analyzed. By generating multiple descriptions and comparing them, the system efficiently identifies the most accurate interpretation without requiring the analyst to manually create or evaluate multiple interpretations, thus maintaining high understanding accuracy while improving productivity
3Measurement precision
If multiple natural language descriptions are generated and compared, then the most accurate description can be selected, but the processing time and computational resources increase
Solution Approach 1:
The system generates multiple natural language descriptions (excessive action) but uses efficient comparison algorithms and distance functions to quickly evaluate them. By using a database of known commands with pre-computed characteristics, the system can partially process multiple descriptions in parallel and quickly identify the best match, thereby achieving high precision without excessive time loss
Data Source
AI summary
In one or more embodiments, a command is repeatedly input a predetermined number of times into a machine learning model to generate a plurality of different natural language (NL) descriptions. The plurality of different NL descriptions are input into the machine learning model to generate a plurality of different check commands. A plurality of similarity metrics are determined by comparing each check command from the plurality of different check commands to the command. A check command from the plurality of different check commands that is most similar to the command is identified based on the plurality of similarity metrics. An NL description from the plurality of different NL descriptions is caused to be displayed, the NL description previously input into the machine learning model to generate the check command.