Network Edge Device Traffic Classification for Security Authorization
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Solution Overview
Problem
Conventional network security solutions fail to effectively authorize and manage network end devices and application services due to static blacklisting, manual rule management, and the inability to scale with dynamic networks, leading to increased latency and bottlenecks, while traditional network infrastructure prioritizes uptime and throughput over security considerations.
Innovation Solution
A network architecture utilizing machine learning for automated device identification and redirection through whitelisting, where endpoint devices are classified into trust levels based on monitored traffic patterns, and traffic is rerouted accordingly, eliminating the need for separate security devices and integrating security into the network infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional security devices (firewalls, ACLs, intrusion prevention systems) are deployed to inspect and control traffic, then security authorization and access control are improved, but network latency increases and throughput decreases due to traffic disruption and inspection bottlenecks
Solution Approach 1:
The patent merges security inspection functions directly into the network switching fabric, eliminating separate security appliances. The security controller integrates with the network switch to perform authorization decisions inline, removing traffic disruption caused by external security devices while maintaining security controls.
Solution Approach 2:
The patent extracts the security inspection logic from traditional inline security appliances and relocates it to a centralized security controller that communicates with the network switch. This allows security decisions to be made without physically disrupting traffic flow through multiple security device inspections.
2Reliability
If manual rule management is used on security devices to control network access, then security policy enforcement is improved, but the system cannot scale with dynamic networks and requires continuous manual updates
Solution Approach 1:
The patent implements automated feedback loops where the security controller continuously monitors network traffic patterns, device behaviors, and service requirements. Based on this feedback, the system dynamically updates authorization decisions and security policies without manual intervention, enabling automatic adaptation to changing network conditions.
Solution Approach 2:
The system performs self-service by automatically discovering new devices, services, and traffic flows, then autonomously making authorization decisions based on learned patterns and policies. This eliminates the need for manual rule management while maintaining security enforcement.
3Reliability
If blacklisting approaches are used to block known bad traffic, then security against known threats is improved, but the system is bypassed by adversaries using simple evasion techniques and cannot detect novel attacks
Solution Approach 1:
The patent inverts the traditional blacklisting approach by implementing a whitelisting system. Instead of blocking known bad traffic, the system automatically identifies and permits legitimate traffic patterns, then blocks everything else. This reversal makes the system inherently more effective against evasion techniques since adversaries cannot bypass a system that permits only authorized behavior.
Solution Approach 2:
The system uses continuous feedback from traffic monitoring to dynamically update its understanding of legitimate versus malicious behavior. By learning from observed traffic patterns over time, the system adapts to new attack vectors and maintains effectiveness against both known and novel threats without relying on static blacklists.
Data Source
AI summary
Systems and methods include monitoring packets, by a network edge device, from one or more endpoint devices where the packets are destined for corresponding application services in a network; classifying the one or more endpoint devices based on the monitoring into a corresponding trust level of a plurality of trust levels; and, responsive to a first endpoint device of the one or more endpoint devices being untrusted, steering the packets from the first endpoint device into a restricted zone.