Authentication Engine for Deviant Intent Detection in Network Functions
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Solution Overview
Problem
Current security measures struggle to detect nefarious actors who gain access to network infrastructure using valid credentials, as they mimic legitimate users, making it difficult to differentiate between normal and malicious activities, especially in complex environments like 5G networks.
Innovation Solution
An authentication engine that utilizes a machine-learning model to determine a user's normal activity range and monitors interactions with network functions, flagging deviant intents by requiring additional authorization for actions outside this range or involving active network functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional security measures are used to monitor network access, then system simplicity is maintained, but the ability to detect nefarious actors with valid credentials deteriorates
Solution Approach 1:
The authentication system is segmented into multiple independent components: traditional credential verification, machine learning model for behavior analysis, deviation detection module, and authorization module. Each component performs a specific function, allowing the system to detect nefarious actors without requiring complete system redesign.
Solution Approach 2:
A machine learning model serves as an intermediary between traditional authentication and security decision-making. The model analyzes user behavior patterns and provides deviation scores that inform authorization decisions, bridging the gap between simple credential checking and complex threat detection.
2Measurement precision
If machine learning models are deployed to detect deviant behavior, then detection precision improves, but processing time and computational resources increase
Solution Approach 1:
User behavior patterns are pre-analyzed during normal operation to establish baseline profiles. When a user performs an action, the system compares it against the pre-established baseline rather than performing full analysis, significantly reducing real-time processing requirements while maintaining detection precision.
Solution Approach 2:
The system performs partial analysis by focusing only on specific behavior dimensions relevant to threat detection rather than analyzing all user actions comprehensively. This selective approach reduces computational overhead while maintaining sufficient detection precision for security purposes.
Data Source
AI summary
Various embodiments of the present technology generally relate to systems and methods for providing an authentication engine for monitoring interactions associated with network functions. In an example, an authentication engine may determine a request from a client device that includes a requested action associated with a network function within a network. The client device may have authentication credentials for performing the requested action. The authentication engine may determine a deviant intent based on the requested action, such as determining the requested action being outside a range of normal activity and/or associated with an active network function. Based on the deviant intent, the authentication engine may generate a subsequent authorization requirement that is required to be completed for the client device to perform the requested action.