Online Data Signing System Aberrant Usage Detection
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Solution Overview
Problem
Existing data signing systems lack a framework to manage permissions flexibly across organizations and are vulnerable to aberrant use, leading to security breaches and revenue loss due to unauthorized access and sharing of subscription credentials.
Innovation Solution
An online data signing system (ODSS) that generates and manages account activity patterns to detect and flag aberrant usage, incorporating a hierarchical organization structure and role-based access to prevent unauthorized data signing and ensure secure, permission-based operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If code signing services are provided online with fee-based models, then productivity and service accessibility are improved, but the system becomes vulnerable to aberrant use and revenue loss
Solution Approach 1:
The system performs preliminary actions by establishing baseline usage patterns for each account before detecting aberrant behavior. It proactively monitors and compares current usage against historical patterns, enabling early detection of potential security threats before they can cause significant harm or revenue loss.
Solution Approach 2:
The system implements continuous feedback loops by monitoring account usage in real-time, comparing it against established baselines, and automatically responding to deviations. This feedback mechanism enables the system to adapt to changing usage patterns while maintaining security through automated anomaly detection and alerting.
2Ease of operation
If subscription credentials are shared among multiple users, then ease of operation is improved, but security and revenue collection are compromised
Solution Approach 1:
The system enables self-service functionality by automatically detecting and responding to aberrant usage patterns without requiring manual intervention. It autonomously monitors usage, identifies anomalies, and triggers appropriate responses such as alerts or account suspensions, reducing the need for human oversight while maintaining security.
Solution Approach 2:
The system replaces manual security monitoring and credential management with automated computational methods. Instead of relying on human operators to detect sharing behavior, the system uses algorithmic analysis of usage patterns to identify aberrant activity, substituting mechanical human processes with automated electronic monitoring.
3Adaptability or versatility
If human-in-the-loop subscriptions are allowed, then service flexibility is improved, but the system becomes susceptible to credential sharing abuse
Solution Approach 1:
The system implements dynamic monitoring that adapts to different subscription types and their expected usage patterns. It adjusts baseline expectations and anomaly detection thresholds based on whether the subscription is human-in-the-loop or machine-to-machine, allowing flexible subscription models while maintaining security through context-aware anomaly detection.
Solution Approach 2:
The system changes monitoring parameters and detection thresholds based on the type of subscription and expected usage patterns. By adjusting sensitivity and baseline parameters according to the specific subscription model, the system maintains flexibility in offering different service types while effectively detecting aberrant behavior specific to each model.
Data Source
AI summary
A system and method for detecting and managing aberrant use of a remote data signing, encryption and obfuscation utility is disclosed. In one embodiment, the method comprises generating a first account activity characteristic pattern associated with the account, the first account activity characteristic pattern generated from execution of one or more first activities associated with the account occurring over a first temporal period, generating a second account activity characteristic pattern, the second account activity characteristic pattern generated from execution of one or more second activities associated with the account occurring over a second temporal period, comparing the first account activity characteristic pattern and the second account characteristic activity pattern, and flagging the account for action according to the comparison.