User Behavior Anomaly Detection via UID Timestamp Ranges
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Solution Overview
Problem
Current security systems struggle to detect malicious user behavior within networks, especially when login information has been compromised, as they primarily focus on prevention methods and do not effectively identify anomalous behavior indicative of malware presence.
Innovation Solution
A network user behavior system that creates user profiles based on activity logs, identifies indicator features, assigns unique identifiers to each combination of features, and establishes a timestamp relationship to detect anomalies outside the range of normal behavior, generating alerts for potential security threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If security systems use traditional prevention methods (firewalls, password rotation), then basic security protection is provided, but they fail to detect anomalous user behavior indicating malware infection
Solution Approach 1:
The system performs preliminary actions by collecting user activity data and establishing baseline behavior patterns before malware infection occurs. User profiles are created in advance containing normal activity ranges, which are then used to detect anomalies when deviations occur, enabling early detection without requiring complex real-time analysis infrastructure
Solution Approach 2:
The patent introduces an intermediary detection layer that sits between traditional prevention systems and user activities. This intermediary system monitors user behavior patterns and compares them against established baselines, providing an additional detection mechanism that doesn't require complete system reconstruction but adds sophisticated anomaly detection capabilities
2Measurement precision
If security systems monitor user behavior in detail, then detection accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system extracts only the most relevant features from user activity data that are indicative of anomalous behavior. By identifying and focusing on key indicator features rather than processing all available data, the system achieves high detection accuracy while minimizing computational overhead and processing time
Solution Approach 2:
The patent applies partial action by monitoring only specific user activities and behaviors that are most likely to indicate malware infection. Rather than analyzing every user action in detail, the system focuses on critical indicators such as unusual login patterns, unexpected file access, or abnormal network communications, achieving effective detection with reduced processing requirements
Data Source
AI summary
A network user behavior system that detects anomalous user behavior includes a memory system with a user behavior module. The user behavior module creates a user profile based on user activity that includes user activity logs that record parameters related to user activity; selects indicator features, wherein the indicator feature includes user activity related to the parameters; creates a user identifier (UID) for each combination of the indicator feature and user; associates each UID with a timestamp to establish a UID and timestamp relationship; establishes a UID and timestamp relationship range indicative of non-anomalous user behavior; and identifies an anomalous user behavior as a UID and timestamp relationship outside of the range indicative of non-anomalous user behavior.