Network Device ML Malicious Traffic Detection
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Solution Overview
Problem
Current network security measures are inadequate in detecting and responding to evolving threats, such as distributed denial of service attacks and unauthorized access, as they rely on outdated strategies that fail to adapt quickly to new attack patterns.
Innovation Solution
Integration of a machine learning model within network devices to analyze telemetry data from various layers of the OSI model, enabling real-time identification and response to malicious or unauthorized network traffic patterns, using techniques like supervised and unsupervised learning, and reinforcement learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional network security measures are used, then device complexity is reduced, but detection precision and response capability to evolving threats deteriorate
Solution Approach 1:
The patent introduces a machine learning model as an intermediary component within the network device that analyzes telemetry data to detect malicious traffic patterns. This model acts as a mediator between raw network data and security decisions, enabling sophisticated detection without requiring complete redesign of the entire network device architecture.
Solution Approach 2:
The patent replaces traditional rule-based and signature-based security mechanisms with machine learning-based detection systems. The ML model processes telemetry data from multiple OSI layers to identify anomalous patterns, substituting mechanical/algorithmic security approaches with intelligent, adaptive detection that improves precision while managing complexity through specialized hardware or software implementations.
2Adaptability or versatility
If machine learning models are integrated into network devices, then adaptability to new attack patterns improves, but device complexity increases
Solution Approach 1:
The patent implements dynamic adaptability by enabling the machine learning model to continuously learn from new telemetry data and evolving attack patterns. The system adjusts its detection capabilities in real-time, allowing the network device to adapt to new threats without requiring manual reconfiguration or complete system redesign, thus improving adaptability while managing complexity through incremental learning.
Solution Approach 2:
The patent changes the operational parameters of the network device by integrating ML models that process multiple telemetry parameters from different OSI layers simultaneously. This multi-parameter analysis approach enables the system to detect complex attack patterns that single-parameter traditional systems miss, improving adaptability while the ML model manages the complexity of processing multiple parameters through learned relationships.
3Speed
If traditional security strategies are used, then device complexity is maintained, but response speed to new threats deteriorates
Solution Approach 1:
The patent implements preliminary action by having the machine learning model continuously analyze telemetry data and detect potential threats before they can cause significant damage. The system performs proactive security monitoring and can initiate mitigation actions in advance, improving response speed by detecting and responding to threats at early stages rather than waiting for confirmed attacks.
Solution Approach 2:
The patent implements feedback mechanisms where the ML model continuously receives telemetry data from the network device, analyzes patterns, and adjusts its detection behavior in real-time. This closed-loop feedback system enables rapid adaptation to new threats and improves response speed by learning from ongoing network conditions and attack patterns without requiring external intervention or complex manual updates.
Data Source
AI summary
Devices and methods to identify malicious usage of a network device. In at least one embodiment, a network device comprises circuitry for performing a networking function and collecting telemetry data indicative of the performance of the networking function. The network device obtains an inference of a network traffic pattern using a machine learning model, and responds to the inference.