Network Traffic Monitoring for Dynamic Threat Access Control
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Solution Overview
Problem
Enterprise networks face challenges in enforcing granular security policies to prevent unauthorized and malicious access to network resources due to a lack of comprehensive threat detection and response mechanisms.
Innovation Solution
A network traffic monitoring system that identifies compromised elements by using sensors to gather and analyze network traffic data, applies dynamic access prevention schemes, and quarantines or blocks access based on detected threats, utilizing machine learning to adapt to evolving threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If enterprises enforce granular security policies to prevent unauthorized access, then network security is improved, but device complexity and difficulty of implementation increase due to lack of comprehensive threat detection mechanisms
Solution Approach 1:
The system segments security policy enforcement into multiple independent components: threat detection module, access control module, and network monitoring module. Each component performs a specific function (detecting threats, controlling access, monitoring traffic) rather than requiring a single complex system to handle all security aspects simultaneously.
Solution Approach 2:
The system performs preliminary threat detection and classification before access control decisions are made. By pre-identifying compromised elements and categorizing threats, the system simplifies subsequent access control enforcement, as decisions are based on pre-analyzed threat data rather than real-time complex analysis.
2Reliability
If comprehensive threat detection mechanisms are implemented, then network security is improved, but loss of time for processing and response increases
Solution Approach 1:
The system applies different levels of detection scrutiny to different network elements based on their risk profiles and behavior patterns. Rather than uniformly analyzing all traffic with maximum depth, it focuses intensive detection resources on suspicious or high-risk elements while using lighter monitoring for normal traffic.
Solution Approach 2:
The system performs partial threat detection on routine traffic (monitoring for obvious signs of compromise) and excessive/detailed analysis only when anomalies are detected. This selective approach maintains security while reducing average processing time across the entire network traffic volume.
3Reliability
If dynamic access prevention schemes are applied to compromised elements, then network security is improved, but device complexity increases due to multiple access control mechanisms
Solution Approach 1:
The system dynamically adjusts access control measures based on real-time threat assessment rather than using static, pre-configured rules. Access prevention schemes are automatically modified as threats evolve, with the system transitioning between different control levels (monitoring, limiting, blocking) based on current compromise status.
Solution Approach 2:
The system changes access control parameters (such as allowed bandwidth, permitted protocols, restricted time windows) rather than simply blocking or allowing access. This allows nuanced control of compromised elements, maintaining security while reducing the need for complete access denial and simplifying policy management.
Data Source
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AI summary
The present disclosure relates to securing workloads of a network by identifying compromised elements in communication with the network and preventing their access to network resources. In one aspect, a method includes monitoring network traffic at network elements of a network; detecting a compromised element in communication with one or more of the network elements, the compromised element being associated with at least one network threat; and based on a defined network policy, applying one of a number of different access prevention schemes to the compromised element to prevent access to the network by the compromised element.