User-Sourced Object Reputation Learning for Enterprise Security
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Solution Overview
Problem
Modern computing ecosystems with always-on broadband connections are vulnerable to attacks due to exposure to the internet, and existing security systems struggle to effectively differentiate between legitimate and malicious objects, particularly those with high novelty and enterprise-specific behavior.
Innovation Solution
A system that utilizes an enterprise-specific machine learning model registry, incorporating user-sourced feedback to adjust the reputation of newly discovered objects, using a machine learning engine to analyze objects, solicit feedback, and integrate it into the model, balancing autonomy and administrator intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional security systems are used to differentiate between legitimate and malicious objects, then security coverage is provided, but false positives increase and administrator workload increases
Solution Approach 1:
The system implements feedback loops where user interactions (clicks, hovers, time spent) on objects are continuously collected and used to retrain machine learning models. This feedback mechanism allows the system to learn from user behavior patterns, improving detection accuracy while reducing false positives, thereby decreasing administrator workload for manual verification.
Solution Approach 2:
The machine learning models automatically analyze user interaction data and perform self-adjustment through continuous retraining. The system serves itself by autonomously improving its detection capabilities without requiring manual intervention or administrator input, thus reducing operational burden while maintaining high reliability.
2Extent of automation
If machine learning models are used to analyze objects, then automation increases, but difficulty in detecting and measuring novel objects increases
Solution Approach 1:
The system employs enterprise-specific machine learning models tailored to detect objects unique to each enterprise environment. These localized models are trained on enterprise-specific data and user interaction patterns, enabling them to effectively identify and measure novel objects within their specific context while maintaining high automation levels.
Solution Approach 2:
The system dynamically adjusts detection parameters and model configurations based on incoming data and user feedback. By changing parameters such as detection thresholds, feature weights, and model architecture, the system adapts to novel objects while maintaining automated operation, overcoming the difficulty of detecting previously unseen enterprise-specific objects.
3Measurement precision
If user feedback is collected and integrated into models, then model accuracy improves, but system complexity increases
Solution Approach 1:
The system implements a universal feedback collection mechanism that handles multiple types of user interactions (clicks, hovers, time spent, explicit feedback) through a single integrated framework. This multi-functional approach collects diverse data types uniformly and processes them through the same machine learning pipeline, improving measurement precision while managing system complexity through consolidation rather than multiplication of components.
Data Source
AI summary
There is disclosed in one example a computing apparatus, including: a hardware platform including a processor circuit and a memory circuit; first means for accessing a machine learning engine; second means for accessing a user interface; and instructions encoded within the memory to instruct the processor to: load into the machine learning engine via the first means an object prevalence model, including an enterprise-specific prevalence model; provide to the machine learning engine an object set from the enterprise; identify an enterprise-novel object from the object set; solicit and receive via the second means user-sourced feedback for the enterprise-novel object; and act according to the user-sourced feedback.