Computer-Augmented Threat Evaluation for Intermediate Code Classification
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Solution Overview
Problem
Existing systems lack effective methods to manage and prioritize intermediate-level security threats in enterprise networks, requiring manual intervention that is often inefficient and inaccurate.
Innovation Solution
An ensemble of machine learning techniques is used to categorize code as safe, unsafe, or intermediate, with human-readable analysis for unclear cases, and a local agent monitors endpoint activities to provide a compact data stream to a central facility for dynamic threat management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated detection techniques are used to evaluate all code samples, then productivity is improved, but measurement precision deteriorates for intermediate threats
Solution Approach 1:
The patent segments code samples into three distinct categories: safe, unsafe, and intermediate threats. This segmentation allows automated machine learning techniques to efficiently process the majority of clear-cut cases while directing human expert analysis specifically to intermediate threats that require higher measurement precision. The segmentation resolves the contradiction by applying different detection approaches to different subsets of the problem space.
Solution Approach 2:
The patent introduces human threat analysts as an intermediary component in the detection system. Human reviewers serve as mediators who provide high-precision evaluation for intermediate threats that automated systems cannot confidently classify. This intermediary approach maintains high productivity for automated processing while ensuring measurement precision is improved for cases where it is most needed.
2Measurement precision
If human reviewers manually evaluate all code samples, then measurement precision is improved, but productivity deteriorates
Solution Approach 1:
The patent segments the workload by classification confidence level, assigning automated machine learning evaluation to high-volume clear-cut cases and human reviewer evaluation specifically to intermediate threats. This segmentation ensures that human reviewers focus their expertise where it provides the most value, maintaining measurement precision without the productivity penalty of manual review for all samples.
Solution Approach 2:
The patent extracts and isolates intermediate threats from the overall code sample population, removing them from automated processing and directing them to human reviewers. This extraction allows the system to maintain high productivity for the majority of samples processed automatically while dedicating human resources specifically to the subset of cases requiring enhanced measurement precision.
3Measurement precision
If comprehensive monitoring of all endpoint activities is implemented, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent implements local quality by having each endpoint device monitor and record all activities locally with full measurement precision, while the central threat management facility selectively processes only the necessary data streams. This approach maintains comprehensive local recording capability without requiring complex real-time processing at every device, thus improving measurement precision while managing device complexity through distributed architecture.
Solution Approach 2:
The patent segments monitoring functions between endpoint devices and the central facility. Endpoint devices perform comprehensive local monitoring and recording, while the central facility performs selective analysis and threat detection. This segmentation allows comprehensive monitoring capability without concentrating all processing complexity in a single system, distributing the complexity burden across the network architecture.
4Device complexity
If filtered data streams are sent to central facility, then device complexity is reduced, but loss of information increases
Solution Approach 1:
The patent applies preliminary action by having endpoint devices perform local filtering and preprocessing of data streams before transmission to the central facility. This preliminary processing reduces device complexity at endpoints while preserving essential threat-relevant information. The filtering is designed to remove only redundant or irrelevant data while maintaining the contextual information necessary for accurate threat detection at the central facility.
Data Source
AI summary
An automated system attempts to characterize code as safe or unsafe. For intermediate code samples not placed with sufficient confidence in either category, human-readable analysis is automatically generated to assist a human reviewer in reaching a final disposition. For example, a random forest over human-interpretable features may be created and used to identify suspicious features in a manner that is understandable to, and actionable by, a human reviewer. Similarly, a k-nearest neighbor algorithm may be used to identify similar samples of known safe and unsafe code based on a model for, e.g., a file path, a URL, an executable, and so forth. Similar code may then be displayed (with other information) to a user for evaluation in a user interface. This comparative information can improve the speed and accuracy of human interventions by providing richer context for human review of potential threats.