Behavior Change Detection for Security Services
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Solution Overview
Problem
Rule-based security threat detection systems struggle with scalability and accuracy, as they require pre-defined patterns and manual coding, leading to missed threats and false positives due to incomplete data and the inability to adapt to system changes.
Innovation Solution
Implementing a behavior change detection system using machine learning techniques that classify security events based on volatility, allowing for iterative learning and real-time anomaly detection, eliminating the need for pre-defined rules and improving scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rule-based security threat detection is implemented, then security events can be detected using pre-defined patterns, but the system fails to scale and produces too many false positives
Solution Approach 1:
The patent replaces the mechanical rule-based system with a machine learning-based system. Instead of using pre-defined patterns and manual rules, the system employs machine learning models that automatically learn security patterns from data, enabling the system to scale without proportionally increasing complexity while maintaining or improving detection accuracy
Solution Approach 2:
The patent changes the fundamental parameters of the detection system by transitioning from static rule-based parameters to dynamic machine learning models that adapt to new threats. This allows the system to handle increasing volumes of security events without requiring proportional increases in rule complexity, thereby improving scalability
2Reliability
If rule-based security monitoring is implemented, then pre-defined patterns can be analyzed, but important security events are missed due to incomplete data
Solution Approach 1:
The patent implements feedback mechanisms where the machine learning system continuously learns from detected security events and adjusts its models accordingly. This feedback loop enables the system to improve its recognition of security events over time, reducing missed detections by incorporating lessons from actual security incidents into the learning process
Solution Approach 2:
The patent performs preliminary actions by training machine learning models on comprehensive security data before deployment. This preliminary training enables the system to recognize patterns and anomalies more effectively from the outset, reducing the likelihood of missing important security events during operation
3Reliability
If machine learning techniques are implemented for behavior change detection, then the system can iteratively learn and reduce false positives, but computational resources and system complexity increase
Solution Approach 1:
The patent segments the machine learning system into distinct components: data collection modules, training modules, inference modules, and update modules. This segmentation allows the complex machine learning functionality to be implemented in a modular manner, making the system more manageable and maintainable while still achieving false positive reduction through iterative learning
Data Source
AI summary
A behavior change detection system collects behavior from a service, such as an online service, and detects behavior changes, either permanent or transient, in the service. Machine learning hierarchical (agglomerative) clustering techniques are utilized to compute deviations between clustered data sets representing an “answer” that the service presents to a series of requests.