Offline Continuous Authentication for DDIL Secure Networks
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Solution Overview
Problem
Existing technologies lack effective methods for authenticating users in secure and isolated network environments, particularly in denied, disrupted, intermittent, and limited-bandwidth (DDIL) environments, where mobile devices with restricted access and processing limitations are disconnected for unknown time ranges.
Innovation Solution
A system and method for authenticating users in DDIL environments using non-transitory computer-readable media that execute instructions to receive and analyze user ID, behavioral, and machine data, comparing them to historical data to determine a likelihood of a threat, and generating offline authorization tokens, with peer-to-peer authentication and blockchain storage for verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional permission server authentication is used, then authentication can be performed with centralized verification, but the system cannot operate in disconnected or isolated network environments
Solution Approach 1:
The authentication system is segmented into two independent components: a permission server for centralized verification and an offline authentication module for disconnected operation. The offline module contains cached credentials and can independently verify user identities without real-time server connection, enabling operation in isolated network environments while maintaining security through periodic synchronization with the permission server.
Solution Approach 2:
Authentication credentials and verification data are pre-loaded into the offline authentication module before disconnection occurs. The system performs preliminary caching of user credentials, cryptographic keys, and verification algorithms, enabling the authentication function to operate autonomously during disconnected periods without requiring real-time server access.
2Adaptability or versatility
If offline authentication is implemented, then the system can operate in disconnected environments, but continuous verification of user actions cannot be performed
Solution Approach 1:
The system implements a feedback mechanism where user actions performed offline are logged and queued for subsequent verification. When the system reconnects to the network, the offline authentication module transmits action logs to the permission server for verification, and receives feedback on whether actions were authorized. This closed-loop feedback ensures continuous verification capability across connected and disconnected periods.
Solution Approach 2:
An action log buffer serves as an intermediary between offline user actions and online verification. The buffer stores pending action records during disconnection, allowing the system to maintain continuous operation while deferring verification until connectivity is restored. This intermediary layer decouples the timing of action execution from verification, enabling seamless operation in disconnected environments.
3Reliability
If multi-factor authentication is required, then security is improved, but authentication complexity and user burden increase
Solution Approach 1:
The authentication system dynamically adjusts the number and type of authentication factors required based on context. In high-risk scenarios or when suspicious activity is detected, the system requires multiple authentication factors (password, biometric, device verification). In low-risk, routine operations, the system accepts simpler authentication methods, reducing user burden while maintaining security through adaptive, risk-based authentication policies.
Data Source
AI summary
Embodiments of the current disclosure are directed to authorizing data transfers and permissions requests in secure networks. In some embodiments, requesting users may request data transfers and access to secure networks, data, resources, documents, and the like. Continuous monitoring, risk analysis, and authorization may be performed in real time in the secure networks by utilizing statistical and machine learning algorithms as well as rules engines to determine a likelihood of the requests being a threat and determine an overall risk level associated with the threat. Furthermore, the secure networks may comprise denied, disrupted, intermittent, and limited-bandwidth (DDIL) DDIL environments that are disconnected from network environments for extended periods. As such, various request authentication techniques may be implemented in the DDIL environments.