Event Feature Distribution Analysis for Security Policy Optimization
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Solution Overview
Problem
Current security systems face inefficiencies in identifying and managing risk associated with user behavior, as they often apply the same security policies indiscriminately, failing to distinguish between normal and anomalous or malicious activities, leading to resource misallocation and potential security breaches.
Innovation Solution
A method and system for analyzing probability distributions of interrelated event features in real-time, which involves receiving event streams, extracting features, constructing distributions, and analyzing them using a processor and storage medium with executable code, enabling dynamic adjustment of security oversight based on user behavior analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If indiscriminate security policies are applied to all user behavior, then security coverage is maintained, but security system resource utilization becomes inefficient
Solution Approach 1:
The patent applies different security policies to different user behaviors based on their risk characteristics. Normal behaviors receive minimal oversight while anomalous behaviors trigger enhanced security measures. This local differentiation optimizes resource allocation by focusing security resources only where needed rather than applying uniform policies across all activities.
Solution Approach 2:
The security system dynamically adjusts its oversight level based on real-time analysis of user behavior patterns. The system transitions from static, uniform security policies to dynamic, adaptive policies that respond to changing behavior characteristics. This allows the system to maintain high security coverage when needed while reducing resource consumption during normal operations.
2Device complexity
If typical security monitoring approaches are used, then system simplicity is maintained, but ability to identify anomalous behavior deteriorates
Solution Approach 1:
The patent replaces traditional rule-based security monitoring with a machine learning-based probabilistic analysis system. Instead of relying on predefined thresholds and static rules, the system uses probabilistic models to analyze user behavior patterns and identify anomalies. This substitution enables more accurate detection of sophisticated threats while maintaining system manageability through automated learning.
Solution Approach 2:
The system changes the fundamental parameters of security monitoring by transitioning from binary (normal/abnormal) classification to probabilistic assessment. By analyzing the likelihood of behaviors rather than applying fixed thresholds, the system achieves higher precision in identifying anomalous activities while adapting to new threat patterns without requiring complex manual rule updates.
3Ease of operation
If uniform security policies are applied, then policy implementation simplicity is maintained, but security effectiveness against different threats deteriorates
Solution Approach 1:
The patent implements dynamic security policies that automatically adapt to different user behaviors and threat patterns. The system maintains ease of operation by automating policy adjustment through machine learning models, eliminating the need for manual policy creation for each scenario. Security effectiveness is improved by applying context-appropriate policies that respond to the specific characteristics of each user activity and potential threat.
Solution Approach 2:
The security system performs self-adjustment by automatically learning from user behavior patterns and refining its security policies without manual intervention. The machine learning models continuously improve their understanding of normal versus anomalous behavior, enabling the system to maintain both operational simplicity and high security effectiveness through autonomous adaptation to new threats and user patterns.
Data Source
AI summary
A method, system and computer-usable medium for constructing a distribution of interrelated event features. The constructing a distribution of interrelated event features includes receiving a stream of events, the stream of events comprising a plurality of events; extracting features from the plurality of events; constructing a distribution of the features from the plurality of events; and, analyzing the distribution of the features from the plurality of events.