Threat Management System Using ML Clustering for Dynamic Access
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Solution Overview
Problem
Current computer security systems rely on rule-based, reactive approaches that are ineffective in dynamically changing environments, failing to timely detect and mitigate cyber threats, and often result in false positives that disrupt legitimate users.
Innovation Solution
A threat management system utilizing a machine learning model that collects features to determine threat types through clustering, enabling proactive and dynamic threat detection, minimizing processing power, and allowing for timely actions to be taken against detected threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If rule-based threshold systems are used to detect attacks, then the system is simple to implement, but it produces false positives that disrupt legitimate users and cannot adapt to dynamically changing threats
Solution Approach 1:
The patent replaces rule-based mechanical threshold systems with a machine learning model that performs clustering analysis. Instead of using fixed rules to detect attacks, the system uses unsupervised learning algorithms to automatically identify patterns and group access attempts by threat type, enabling adaptability to evolving threats without manual rule updates.
Solution Approach 2:
The system changes from static threshold parameters to dynamic cluster-based parameters. The machine learning model continuously processes access attempt data and automatically adjusts clustering parameters based on observed patterns, allowing the system to adapt to new threat types and behaviors without requiring manual parameter tuning.
2Productivity
If predefined thresholds are used for failed login attempts, then the system requires minimal processing resources, but it cannot timely detect and mitigate sophisticated cyber threats
Solution Approach 1:
The system applies partial action by performing clustering analysis only on access attempts that meet certain criteria or show suspicious patterns, rather than analyzing every single access attempt in detail. This selective approach maintains high detection speed while reducing overall processing power consumption.
Solution Approach 2:
The machine learning model is pre-trained on historical attack data before deployment. This preliminary training enables the system to quickly recognize and cluster new threats in real-time without requiring intensive processing during actual threat detection, as the clustering logic has already been learned during the training phase.
3Measurement precision
If simple threshold-based lockout is implemented, then the system is easy to operate, but it results in false positives that lock out legitimate users
Solution Approach 1:
The patent replaces simple threshold-based detection with machine learning-based clustering analysis. The system automatically identifies threat types by grouping access attempts based on multiple features and patterns, significantly improving identification accuracy while reducing false positives that incorrectly flag legitimate users as threats.
4Measurement precision
If comprehensive feature collection is performed for threat analysis, then the system achieves high accuracy in threat type determination, but it increases processing time and resource usage
Solution Approach 1:
The system collects and processes only the most relevant features for clustering analysis rather than analyzing all possible data points. The machine learning model is designed to work efficiently with a focused set of key features, maintaining high threat type determination accuracy while minimizing processing time and resource consumption.
Data Source
AI summary
A computer-implemented method, computer system, and computer program product for threat management. A set of features used by a machine learning model is collected by the computer system to determine a threat type for an access attempt when the access attempt is detected. A cluster is determined, by the machine learning model in the computer system, for the access attempt using the set of features, wherein the machine learning model implements clustering to determine the cluster for the access attempt, and wherein the cluster for the access attempt corresponds to the threat type for the access attempt. A set of actions is performed by the machine learning model in the computer system based on the threat type determined for the access attempt.