Decision Tree Command Similarity for Faster Malicious Command Triage
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Solution Overview
Problem
Cybersecurity analysts lack the knowledge to understand complex computing commands effectively, leading to inefficient and inaccurate decision-making, as they often rely on insufficient sample sizes and manual analysis of similar commands, which can result in missed or false flags.
Innovation Solution
A database of decision trees is generated to analyze and provide context for computing commands, using machine learning to identify similar commands and behaviors, and display probabilities and case notes, enabling faster and more accurate decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If analysts manually analyze commands to understand computing commands, then they can identify similar commands, but the process is slow, inefficient, and inaccurate
Solution Approach 1:
The patent replaces the manual mechanical analysis process with an automated machine learning system. The ML model automatically analyzes computing commands, extracts features, and identifies similar commands without human intervention, thereby eliminating the time loss and inefficiency associated with manual analysis while maintaining or improving accuracy.
Solution Approach 2:
The patent introduces a decision tree database as an intermediary structure between the raw command data and the analysis results. This database pre-organizes commands and their features, enabling faster retrieval and comparison during analysis, thus reducing the time required while improving the precision of identifying similar commands.
2Quantity of substance
If analysts rely on insufficient sample sizes of previous commands, then manual analysis is manageable, but the accuracy and comprehensiveness of identification deteriorates
Solution Approach 1:
The patent creates a universal decision tree database that can handle and organize large volumes of diverse computing commands from multiple sources. This database structure enables the system to process extensive sample sizes efficiently, allowing for more comprehensive and accurate identification of similar commands by drawing from a larger, more varied dataset.
3Measurement precision
If a comprehensive database of similar commands is created to improve analysis accuracy, then identification precision improves, but system complexity increases
Solution Approach 1:
The patent segments the comprehensive command database into a hierarchical decision tree structure, where commands are organized by their features and characteristics. This segmentation allows the system to manage complexity by breaking down the large database into smaller, more manageable subsets that can be queried efficiently, thereby maintaining high identification accuracy without overwhelming system complexity.
Solution Approach 2:
The patent transforms the raw command data into a standardized feature-based representation within the decision tree database. By changing the parameters of command storage from raw text to structured feature vectors, the system enables more efficient processing and comparison, improving identification accuracy while reducing the operational complexity of managing the database.
4Productivity
If machine learning models are used to automatically analyze commands, then analysis speed and comprehensiveness improve, but the complexity of the system increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing commands and pre-organizing them into the decision tree database structure before actual analysis occurs. Features are extracted and commands are categorized in advance, which reduces the computational burden during runtime analysis. This preliminary preparation increases analysis speed while managing system complexity by shifting some processing to an offline phase.
Data Source
AI summary
A potentially malicious command including a plurality of features is received. Additionally, a plurality of nodes included in a decision tree are traversed, based on the plurality of features, to identify a leaf node included in the plurality of nodes. The leaf node is associated with (1) a first set of similar commands, each similar command from the first set of similar commands including the plurality of features, and (2) a second set of similar commands from the first set of similar commands and that were previously detected. Additionally, a probability that the potentially malicious command will be escalated as potentially malicious is determined based on the first set of similar commands and the second set of similar commands. Additionally, a first indication quantifying the first set of similar commands, a second indication quantifying the second set of similar commands, and the probability are caused to be displayed.