Post-Authentication User Verification via Interaction Profiling
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Solution Overview
Problem
Conventional cybersecurity solutions, such as antivirus and firewall systems, often fail to keep pace with new threats in industrial environments, making it difficult for organizations to protect their networks and systems from intrusions, as they primarily rely on known threats and configurations, leaving vulnerabilities unaddressed.
Innovation Solution
Implementing post-authentication user verification based on user interactions by collecting metadata during valid user sessions to create profiles of typical interactions, which are then compared to subsequent sessions to detect anomalies, indicating potential malicious activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional cybersecurity solutions (antivirus, firewall) are used, then protection against known threats is provided, but they cannot keep pace with new threats and leave vulnerabilities unaddressed
Solution Approach 1:
The system performs preliminary actions by collecting user interaction data during authenticated sessions and generating behavioral profiles before threats occur. These profiles establish a baseline of normal user behavior that enables the system to detect anomalies and potential threats in real-time, rather than relying solely on post-incident response or pre-configured security rules.
Solution Approach 2:
The system implements continuous feedback loops where user interactions are monitored, compared against established profiles, and used to dynamically adjust security assessments. When anomalies are detected, the system can trigger additional verification steps or alerts, creating a responsive security mechanism that adapts to observed behavior patterns rather than relying on static security configurations.
2Measurement precision
If post-authentication user verification based on interaction analysis is implemented, then detection of unauthorized access is improved, but system complexity increases
Solution Approach 1:
The system employs self-service mechanisms by automatically collecting user interaction data, generating behavioral profiles, and performing anomaly detection without requiring manual configuration or intervention. The profiles are created and maintained autonomously based on observed user patterns, reducing the operational burden while maintaining high validation accuracy.
Solution Approach 2:
The system applies partial verification by focusing on specific interaction patterns and behaviors that are most indicative of unauthorized access, rather than analyzing every possible user action. This selective approach maintains high detection precision while avoiding the complexity of comprehensive analysis of all user activities.
Data Source
AI summary
A method includes obtaining (702) first data identifying first user interactions with one or more computing or networking resources during at least one first user session that is known to be valid. The method also includes generating (704) one or more profiles defining typical user interactions with the one or more resources based on the first data. The method further includes obtaining (708) second data identifying second user interactions with at least one of the one or more resources during a subsequent second user session. The method also includes determining (712) whether the second user session is valid based on the second data and at least one of the one or more profiles by comparing the second user interactions to the typical user interactions defined in the at least one profile. In addition, the method includes taking (714, 716) one or more actions in response to determining that the second user session is not valid.