Dynamic User Baseline for Network Intrusion Detection
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Solution Overview
Problem
Traditional security systems rely on static rules to detect intrusions, which are insufficient for capturing the differences in patterns of activities among users in a network and struggle to detect sophisticated fraudulent attacks, as they require complex conditions that are difficult to specify and result in an exponential number of rules for sequence deviations.
Innovation Solution
A network security system generates a user-specific baseline of temporally-ordered sequences of events and time gaps, allowing for dynamic rule evaluation to detect anomalies by comparing current session events against the reference baseline, thereby flagging significant deviations as potential fraud.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static rules are used to detect intrusions, then the system is simple to implement, but it cannot capture differences in user activity patterns and fails to detect sophisticated fraudulent attacks
Solution Approach 1:
The patent transforms static intrusion detection rules into dynamic user-specific baselines that adapt to individual user behavior patterns. The system continuously learns and updates baseline activity patterns for each user, enabling dynamic adjustment of detection thresholds and rules based on observed behavior, thereby improving detection accuracy without requiring manually complex rule specifications.
Solution Approach 2:
The system automatically generates and updates user-specific baseline patterns without requiring manual rule configuration. The baseline generation module autonomously analyzes user activity data, identifies normal behavior patterns, and maintains these baselines, eliminating the need for security personnel to manually create and maintain complex detection rules for each user.
2Reliability
If complex conditions are specified to detect sophisticated fraudulent attacks, then detection capability improves, but the number of rules increases exponentially
Solution Approach 1:
Instead of creating numerous complex rules to cover all possible fraud scenarios, the system creates a single representative baseline pattern for each user that captures their normal behavior. This baseline serves as a template against which all future user activities are compared, eliminating the need for exponential rule proliferation while maintaining comprehensive detection capability.
Solution Approach 2:
The system changes the approach from fixed rule conditions to dynamic parameter-based baselines. Rather than specifying complex static conditions, the system uses learned parameters from actual user behavior (timing patterns, sequence patterns, frequency patterns) to automatically determine what constitutes anomalous activity, thereby reducing rule complexity while improving detection accuracy.
3Measurement precision
If user-specific baseline analysis is implemented, then detection accuracy for sophisticated attacks improves, but system complexity increases
Solution Approach 1:
The system segments the intrusion detection problem into distinct functional modules: event collection module, baseline generation module, and anomaly detection module. Each module handles a specific aspect of the analysis, making the overall complex system manageable through clear separation of concerns. The baseline generation module specifically segments user activity into discrete event sequences that can be independently analyzed and compared.
Data Source
AI summary
Systems and methods for evaluation of events are provided. A user-specific reference baseline comprising a set of temporally-ordered sequences of events. An event of a sequence of events in a current session is received. A determination is made as to whether the event at least partially matches the reference baseline using an attribute of the event and a temporal position of the event within the sequence of events in the current session.