Network Flow Clustering for Predictive Cyber Threat Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current cybersecurity algorithms are largely reactive and require significant human expertise to analyze network flow data for identifying and mitigating cyber threats, often producing false positives and being unable to predict future attacks effectively.
Innovation Solution
A system and method that classifies network flow data by clustering similar traffic patterns and assigning labels, using machine learning to identify and predict cyber threats by comparing with known malicious traffic, enabling rapid threat detection and proactive defense.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human analysts manually analyze network flow data, then threat identification accuracy is improved, but analysis time and operational complexity increase significantly
Solution Approach 1:
The system enables self-service threat detection by automatically classifying network flow data through clustering algorithms and machine learning models. The network itself analyzes its own traffic patterns without requiring constant human intervention, with the system autonomously identifying threats and generating alerts based on learned behaviors and known threat signatures.
Solution Approach 2:
The patent replaces the mechanical manual analysis process with automated computational systems. Machine learning models, clustering algorithms, and pattern recognition systems substitute for human analyst workflows, enabling rapid processing of network flow data that would be impossible to handle manually at scale.
2Device complexity
If reactive cybersecurity algorithms are used, then implementation complexity is reduced, but ability to predict future attacks deteriorates
Solution Approach 1:
The system performs preliminary actions by continuously training machine learning models on network traffic patterns and threat intelligence data. The clustering algorithms pre-organize traffic into categories and the system maintains updated profiles of legitimate versus malicious behavior, enabling prediction of future attacks before they manifest as actual threats.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously learns from new threat data and adjusts its classification models. Threat intelligence feeds back into the system to refine future detections, creating an adaptive loop that improves prediction capability over time while managing implementation complexity through automated model updates.
3Ease of operation
If existing cybersecurity algorithms are used, then ease of operation is maintained, but false positive rate increases
Solution Approach 1:
The system changes the parameters of threat detection by using multiple classification models with different decision thresholds and weighting schemes. By adjusting model sensitivity, confidence thresholds, and the relative importance of different threat indicators, the system optimizes the balance between detection accuracy and false positive reduction while maintaining ease of operation through automated parameter tuning.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, receiving information about known malicious activity, the information about known malicious activity corresponding to a known cyber threat to operation of a network or data processing system, using a clustering process to label flow data for identifying the information about known malicious activity, receiving production flow data corresponding to current network traffic arriving at the network or the data processing system, determining clusters and cluster identifiers for the production flow data, identifying a relationship between a cluster label for the information about known malicious activity and a cluster identifier for the production flow data, and based on the relationship between the cluster label for the information about known malicious activity and the cluster identifier, identifying a potential cyber threat to the network or the data processing system. Other embodiments are disclosed.