Context-Aware Access Control Anomaly Detection
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Solution Overview
Problem
Current access control systems in computing environments face challenges in securely managing access to protected resources and detecting anomalies in access control events, particularly in scenarios where user behavior deviates from established patterns, such as accessing resources from new locations or using new networks.
Innovation Solution
An access control system that acquires contextual information associated with access requests, compares it to a baseline set of rules generated from prior access history, detects deviations, and requires additional authentication information before authorizing access, while updating the baseline rules if no intrusion is detected within a threshold period.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If additional authentication information is required for access requests with contextual deviations, then security against unauthorized access is improved, but access control system complexity and processing time increase
Solution Approach 1:
The system pre-establishes baseline access patterns for each user including typical locations, devices, and access times. When a new access request arrives, the system compares it against these pre-established baselines and only triggers additional authentication when deviations are detected, rather than requiring authentication for all accesses.
Solution Approach 2:
The system applies different authentication requirements to different access scenarios based on their risk profiles. Access requests that match established user patterns receive standard authentication, while only those with contextual deviations (different location, device, or time) trigger enhanced authentication, creating localized security responses rather than uniform treatment.
2Adaptability or versatility
If baseline rules are updated frequently to reflect changing user behavior, then adaptability to legitimate behavior changes is improved, but detection of actual intrusions worsens due to baseline drift
Solution Approach 1:
The baseline rules are designed to be dynamic and adaptive, automatically updating when legitimate behavior patterns change. The system learns new locations, devices, and access times as they occur, allowing the baseline to evolve with user needs while maintaining security through continuous adaptation rather than static rules.
Solution Approach 2:
The system incorporates feedback loops where access outcomes and contextual information are continuously fed back into the baseline establishment process. This allows the system to learn from both successful legitimate accesses and blocked suspicious activities, refining its detection accuracy over time while adapting to genuine behavior changes.
Data Source
AI summary
Methods for managing access to protected resources within a computing environment and detecting anomalies related to access control events are described. An access control system may acquire a request for access to a protected resource, identify a username associated with the request, acquire contextual information associated with the request for access (e.g., a time of day associated with a location of a device making the request), acquire a baseline set of rules for the username, detect a deviation from the baseline set of rules based on the contextual information, acquire additional authentication information in response to detecting the deviation, authorize access to the protected resource based on the additional authentication information, generate a record of the request for access including the contextual information, and update the baseline set of rules if an intrusion to the access control system has not been detected within a threshold period of time.