Cloud Traffic Rule Confidence Using IP Similarity Stability
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Solution Overview
Problem
Securing cloud networks against malicious attacks is challenging due to the virtual and code-based nature of machines, necessitating improved security measures.
Innovation Solution
Implementing a traffic controller that monitors network traffic, generates rules based on data logs, and applies confidence measures to determine and enforce communication restrictions, using IP address analysis and similarity algorithms to stabilize traffic rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traffic rules are generated based on data logs to secure cloud networks, then security against malicious attacks is improved, but the complexity of the system increases due to the need to analyze and determine traffic directions
Solution Approach 1:
The system automatically analyzes data logs, determines traffic directions, and generates security rules without requiring manual configuration or intervention. The traffic controller autonomously processes network traffic data, identifies patterns, and creates enforcement rules, making the security system self-configuring and reducing operational complexity.
Solution Approach 2:
The patent introduces an intermediary component that processes and interprets data logs to determine traffic directions between machines. This intermediary layer translates raw network data into meaningful traffic direction information, which then feeds into rule generation, simplifying the overall system architecture by creating a dedicated processing layer.
2Measurement precision
If the system monitors and analyzes all network traffic data logs to determine traffic directions, then accuracy of traffic direction determination is improved, but the time required for analysis and rule generation increases
Solution Approach 1:
The system extracts only the essential and relevant information from data logs needed for determining traffic directions, rather than processing every detail of the traffic data. By focusing on key indicators such as source and destination addresses and traffic flow patterns, the system achieves accurate determination while reducing processing time.
Solution Approach 2:
The patent applies partial action by analyzing a representative subset of data logs or focusing on critical traffic patterns rather than exhaustively processing every single data point. This approach maintains sufficient accuracy for security rule generation while significantly reducing the time required for analysis.
3Reliability
If confidence measures are applied to traffic rules to filter out unstable rules, then reliability of security enforcement is improved, but the complexity of rule validation increases
Solution Approach 1:
The patent replaces complex manual validation processes with automated computational methods for calculating confidence measures. Instead of requiring manual review of each traffic rule's stability, the system uses algorithms to automatically assess confidence levels based on traffic pattern consistency, data log frequency, and rule stability metrics, simplifying the validation process while maintaining high reliability.
Data Source
AI summary
In some instances, a method for generating network traffic rules for a cloud environment is provided. The method comprises: converting a plurality of internet protocol (IP) addresses from a plurality of network traffic rules into a plurality of integer pair representations of the IP addresses; determining a plurality of similarity values based on the plurality of integer pair representations of the IP addresses and a similarity algorithm; determining stability of one or more of the plurality of network traffic rules based on the plurality of similarity values and one or more thresholds; and applying the one or more of the plurality of network traffic rules that are determined to be stable.