Machine-Learned Command Line Interpretation for Threat Triage
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Solution Overview
Problem
Human experts face significant challenges in efficiently and timely assessing millions of complex cybersecurity command lines and process trees due to their complexity, requiring substantial time and skill, leading to a mounting backlog in threat detection.
Innovation Solution
A cloud-based machine-learned cybersecurity command line interpretation service that uses artificial intelligence and machine learning to simplify and quickly assess command lines, providing plain-language explanations and predictions of malicious or benign activity, with the option to retrieve historical interpretations to conserve resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human experts manually inspect and assess cybersecurity command lines, then assessment accuracy and skill are improved, but time consumption and productivity are worsened
Solution Approach 1:
The patent introduces an intermediary system consisting of a command line interpretation service and machine learning model that stands between the raw command line data and human expert assessment. This intermediary automatically interprets complex command lines into simplified representations and provides initial assessments, thereby reducing the burden on human experts while maintaining accuracy through human review of critical cases.
Solution Approach 2:
The patent replaces the manual mechanical process of human experts reading and analyzing complex command lines with an automated electronic system. The machine learning model processes command lines algorithmically, transforming the manual inspection process into an automated computational process that can handle millions of command lines without human intervention for routine cases.
2Reliability
If human experts manually assess command lines, then assessment thoroughness is improved, but time required and duration of action are worsened
Solution Approach 1:
The patent applies preliminary action by having the command line interpretation service and machine learning model perform initial assessment and interpretation before human experts review the data. The system pre-processes millions of command lines, identifies potential threats, and prepares summarized information, so that when human experts do review, they are only looking at pre-sorted priority items rather than raw data from scratch.
Solution Approach 2:
The intermediary machine learning system acts as a filter and preparator between the raw command line data and human expert review. It translates complex command lines into simplified interpretations and prioritizes items based on detected threats, thereby reducing the time human experts need to spend while maintaining thoroughness through targeted review of critical items.
3Adaptability or versatility
If the volume of cybersecurity detections increases, then detection capability is improved, but resource consumption and complexity are worsened
Solution Approach 1:
The patent segments the cybersecurity assessment system into distinct functional components: a command line interpretation service that handles data translation, a machine learning model that performs pattern recognition and threat detection, and a human expert review layer for validation. This segmentation allows each component to specialize in specific tasks, making the overall system more manageable and scalable despite increasing detection volumes.
Solution Approach 2:
The machine learning model serves as an intermediary layer that processes the increasing volume of command line data between data collection and human analysis. It automatically handles the complexity of high-volume data processing, pattern recognition, and initial threat assessment, thereby enabling the system to scale detection capability without proportionally increasing human resource requirements or operational complexity.
4Measurement precision
If manual inspection of command lines is performed, then assessment accuracy is improved, but resource consumption and cost are worsened
Solution Approach 1:
The system applies self-service by having the machine learning model and command line interpretation service automatically perform initial assessment and translation of command lines without requiring continuous human intervention. The system serves itself by using automated processes to handle routine assessment tasks, reserving human resources only for complex cases that require expert judgment, thereby significantly reducing overall resource consumption while maintaining accuracy.
Solution Approach 2:
The patent replaces the resource-intensive manual inspection process with an automated electronic system that uses machine learning algorithms and computational processing. This substitution dramatically reduces the human resources and time required for assessment, converting a labor-intensive process into an efficient automated system that maintains accuracy through intelligent algorithms and human-in-the-loop validation for critical cases.
Data Source
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AI summary
A cloud-based, machine-learned cybersecurity command line interpretation service simplifies complex command lines using plain language. Command lines are input to the cybersecurity command line interpretation service for an interpretation by a machine learning model. If, however, a command line is known and been previously interpreted, then the cybersecurity command line interpretation service may conserve hardware and software resources by retrieving a historical command line interpretation. If the command line is unknown or not historically logged, then the cybersecurity command line interpretation service may generate a current command line interpretation using the machine learning model. The cybersecurity command line interpretation service may then generate a cybersecurity prediction associated with the command line based on the historical or current command line interpretation. The cybersecurity command line interpretation service thus provides a much faster interpretation and cybersecurity prediction for assessing command lines as malicious or benign.