Endpoint Device Unauthorized Use Detection via Machine Learning
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Solution Overview
Problem
Endpoint devices can be vulnerable to malicious use after being lost or stolen, with unauthorized users potentially causing harm before administrators can implement security measures, due to delays in reporting such incidents.
Innovation Solution
A computing device employs a machine learning model to detect unauthorized use by comparing usage data with normal usage patterns, allowing it to prevent access to the computing environment through actions like deleting data or disabling authentication tokens, even when the device is disconnected from the enterprise server.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If administrator security measures are implemented after device loss, then device security is improved, but the time window for malicious use increases
Solution Approach 1:
The system performs preliminary actions by establishing baseline usage patterns before device loss occurs. The machine learning model continuously learns and stores normal usage behaviors, enabling immediate detection of unauthorized use without waiting for administrator intervention or user reporting.
Solution Approach 2:
The system implements continuous feedback by monitoring device usage in real-time and comparing it against established baselines. When abnormal patterns are detected, the system immediately triggers security responses, creating a closed-loop feedback mechanism that eliminates the time delay between device loss and security intervention.
2Loss of time
If user reports device loss immediately, then security response time is improved, but user ability to communicate is worsened
Solution Approach 1:
The system performs self-service by autonomously detecting unauthorized use and initiating security measures without requiring user action. The machine learning model continuously monitors device usage patterns and automatically triggers security responses, eliminating the need for users to report device loss through potentially unavailable communication channels.
Solution Approach 2:
The system introduces an intermediary mechanism - the machine learning model - that acts as a mediator between device usage and security responses. This intermediary continuously analyzes usage patterns and triggers security measures independently, bypassing the need for direct user-to-administrator communication.
3Measurement precision
If machine learning model continuously monitors usage, then unauthorized use detection is improved, but computational resource consumption increases
Solution Approach 1:
The system applies partial action by focusing computational resources only on detecting deviations from established baselines rather than analyzing every usage event. The machine learning model compares current usage against pre-established patterns, requiring minimal computational overhead for anomaly detection while maintaining high precision.
Solution Approach 2:
The system performs preliminary computational work by establishing usage baselines during normal operation phases. This preliminary action creates reference patterns that enable efficient real-time detection with minimal ongoing computational resources, as the heavy lifting of pattern recognition is done during baseline establishment rather than continuous monitoring.
Data Source
AI summary
Methods and systems for preventing malicious use of endpoint devices are described herein. A computing device may receive data indicative of usage of the computing device by a user. The computing device may compare the received data with other data (indicative of how an authorized user for the computing device uses the computing device) stored on the computing device to identify instances of abnormal usage of the computing device. The computing device may detect unauthorized use of the computing device based on the number of instances of abnormal usage exceeding a threshold. The computing device may prevent access to a computing environment with use of the computing device in response to detection of unauthorized use.