International business machines corporation (20240137375). FOUNDATIONAL MODEL FOR NETWORK PACKET TRACES simplified abstract

FOUNDATIONAL MODEL FOR NETWORK PACKET TRACES

Organization Name

international business machines corporation

Inventor(s)

MUDHAKAR Srivatsa of White Plains NY (US)

Davis Wertheimer of White Plains NY (US)

Franck Vinh Le of West Palm Beach FL (US)

Utpal Mangla of Toronto (CA)

SATISHKUMAR Sadagopan of Leawood KS (US)

Mathews Thomas of Flower Mound TX (US)

Dinesh C. Verma of New Castle NY (US)

FOUNDATIONAL MODEL FOR NETWORK PACKET TRACES - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240137375 titled 'FOUNDATIONAL MODEL FOR NETWORK PACKET TRACES

Simplified Explanation

The patent application describes a technique for using a foundational model for network packet traces, involving extracting features from network traffic, generating tokens from the features, training a machine learning model to output contextual embeddings for the tokens, and using the embeddings to detect anomalies in the network traffic.

• Extract features from network traffic
• Generate tokens from the features
• Train a machine learning model to output contextual embeddings for the tokens
• Use the embeddings to detect anomalies in the network traffic

Potential Applications

This technology could be applied in network security systems to detect and prevent cyber attacks, in network monitoring tools to identify performance issues, and in network optimization solutions to improve overall network efficiency.

Problems Solved

This technology helps in identifying anomalies in network traffic that could indicate security breaches, performance issues, or inefficiencies in the network. By using machine learning models to analyze network data, potential threats can be detected and addressed proactively.

Benefits

- Enhanced network security - Improved network performance - Proactive detection of potential issues

Potential Commercial Applications

"Enhancing Network Security and Performance with Foundational Model for Network Packet Traces"

Possible Prior Art

One possible prior art could be traditional network monitoring tools that rely on rule-based systems to detect anomalies in network traffic. However, these systems may not be as effective or efficient as machine learning-based approaches in detecting complex and evolving threats in real-time.

How does this technology compare to existing network security solutions?

This article does not provide a direct comparison to existing network security solutions, leaving a gap in understanding the competitive advantages of this technology.

What are the limitations of using machine learning models for network traffic analysis?

The article does not address the potential limitations or challenges of using machine learning models for network traffic analysis, such as scalability, interpretability, or false positive rates.

Original Abstract Submitted

embodiments related to using a foundational model for network packet traces. a technique includes receiving network traffic of a network and extracting features from the network traffic, the features having a function related to communications in the network. the technique includes generating tokens from the features, each of the features corresponding to a respective one of the tokens, training a machine learning model by inputting the tokens, the machine learning model being trained to output contextual embeddings for the tokens, and using the contextual embeddings to determine an anomaly in the network traffic.