Network Traffic Embeddings for Distributed Cyberattack Detection

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Solution Overview

Problem

Conventional systems struggle to identify cyberattacks, particularly those mimicking non-malicious network activity over extended periods, making real-time detection challenging.

Innovation Solution

A transformer encoder-based system analyzes network traffic over time to determine feature values, generating embeddings and using a detection head to identify cyberattacks by comparing probabilities against thresholds, enabling quick detection and isolation of malicious activity.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If conventional systems monitor network activity for sharp increases in activity, then they can identify some attacks, but they fail to detect attacks distributed over longer periods that mimic non-malicious activity

Engineering Contradiction:

Improvedetection accuracyVSAvoidability to detect distributed attacks

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The system dynamically adjusts its detection approach by analyzing network traffic at multiple temporal scales. It examines both short-term sharp increases and long-term distributed patterns, allowing the detection mechanism to adapt to different attack distributions and timeframes rather than relying on a fixed monitoring threshold

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system adds a temporal dimension to attack detection by analyzing network traffic patterns across extended periods. Instead of only monitoring for immediate sharp increases, it incorporates historical traffic data and temporal relationships to detect attacks that distribute malicious activity over longer timeframes, effectively adding time as an additional analytical dimension

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

2Speed

If systems analyze network traffic in real-time, then they can respond quickly to attacks, but they struggle to detect attacks configured to mimic non-malicious activity over extended periods

Engineering Contradiction:

Improvedetection speedVSAvoiddetection reliability

Core Design Contradiction:

SpeedVSReliability

Solution Approach 1:

The system performs preliminary analysis by continuously collecting and storing network traffic data over extended periods before actual attacks occur. This pre-collection of baseline traffic patterns enables the system to quickly compare incoming traffic against historical norms, allowing both rapid detection and reliable distinction between malicious and benign distributed activity

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback mechanisms that continuously refine its detection models using analyzed traffic patterns. By feeding back historical attack patterns and false positive information into the detection algorithm, the system improves its ability to reliably distinguish malicious distributed attacks from legitimate traffic while maintaining fast detection speeds

Inventive Principle:

Principle #23Feedback

3Device complexity

If conventional systems use simple activity threshold monitoring, then the system complexity remains low, but the systems cannot identify attacks distributed over longer periods

Engineering Contradiction:

Improvemonitoring system complexityVSAvoidattack identification accuracy

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The system segments network traffic analysis into multiple temporal components, examining traffic patterns at different time scales simultaneously. It divides the analysis into short-term fluctuations, medium-term trends, and long-term patterns, allowing complex attack detection to be broken into manageable analytical segments that can be processed with enhanced precision without overwhelming system complexity

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS20250373638A1Systems and methods for monitoring network traffic to identify cyberattacks

Publication Date: 2025.12.04 STRIPE LLC

AI summary

Disclosed herein are systems and methods for monitoring network traffic to identify cyberattacks. An example method includes obtaining a first plurality of data packets transmitted over a network during at least one period of time; determining a plurality of feature values based on the plurality of data packets, each feature corresponding to execution of a protocol by the first system; generating an embedding based on the plurality of feature values and an encoder, the encoder configured to generate embeddings based on pluralities of feature values, the embeddings corresponding to a period of time; and determining that at least one data packet of the plurality of data packets is involved in a cyberattack based on the embedding. The method can include causing an execution of a remedial protocol in association with the first system.