Temporal Anomaly Detection for Network Security
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Solution Overview
Problem
Existing network security systems are unable to detect network attacks in a timely manner, allowing unauthorized access and malicious activities such as data exfiltration or malware uploads, due to their inability to identify unauthorized communication channels until after an attack has occurred.
Innovation Solution
A machine learning-based system that identifies clusters of user devices and communication channels within a network, using group information and user interaction data to detect anomalies and unauthorized communication channels, allowing for real-time prevention of malicious activities by modifying settings on affected devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional network security systems are used, then device complexity is reduced, but detection speed and accuracy of unauthorized communication channels deteriorate
Solution Approach 1:
The patent introduces a machine learning model as an intermediary component between network data and security analysis. This model processes user interaction data and communication patterns to identify unauthorized channels, enabling faster detection without requiring complex manual analysis systems.
Solution Approach 2:
The patent replaces traditional mechanical rule-based security systems with machine learning-based automated detection. The machine learning model automatically analyzes network traffic patterns and user behavior, substituting manual security monitoring mechanisms with intelligent automated systems that provide faster response times.
2Measurement precision
If machine learning-based anomaly detection is implemented, then detection accuracy of unauthorized communication channels improves, but device complexity increases
Solution Approach 1:
The patent implements preliminary action by pre-training machine learning models with historical network data and establishing baseline communication patterns before actual security threats occur. This preparation enables the system to quickly and accurately detect anomalies without requiring complex real-time analysis of every data point.
Solution Approach 2:
The machine learning model performs self-service by automatically learning from network data, identifying patterns, and detecting unauthorized communication channels without requiring constant human intervention or complex configuration. The system self-optimizes its detection capabilities through continuous learning from incoming data.
3Reliability
If real-time monitoring of user devices is performed, then network security improves, but network resources and throughput are consumed
Solution Approach 1:
The patent applies partial action by monitoring only specific user interactions and communication patterns that are relevant to security threats, rather than analyzing all network traffic in real-time. The machine learning model focuses on key indicators such as unusual communication frequencies, unauthorized device connections, and abnormal data transfer patterns, reducing overall resource consumption while maintaining security effectiveness.
Data Source
AI summary
A device configured to obtain a first user interaction data at a first time instance for user devices, to obtain a first set of clusters from a machine learning model based on the first user interaction data, and to determine a first cluster quantity for the first set of clusters. The device is further configured to obtain a second user interaction data at a second time instance for the user devices, to obtain a second set of clusters from the machine learning model based on the second user interaction data, and to determine a second cluster quantity for the second set of clusters. The device is further configured to determine the second cluster quantity is greater than the first cluster quantity, to identify a cluster that is not present in the first set of clusters, and to modify settings on a user device from within the cluster.