Gemma Scope is a research tool for analyzing and understanding the inner workings of the Gemma 2 generative AI models, allowing examination of individual AI model layers during request processing.
This paper explores the structure of the feature point cloud discovered by sparse autoencoders in large language models. It investigates three scales: atomic, brain, and galaxy. The atomic scale involves crystal structures with parallelograms or trapezoids, improved by projecting out distractor dimensions. The brain scale focuses on modular structures, similar to neural lobes. The galaxy scale examines the overall shape and clustering of the point cloud.
This article explains how Palo Alto Networks uses autoencoders to profile DNS traffic and detect malicious domains based on unique patterns and characteristics.
Problem: Malicious DNS traffic often exhibits unique patterns that can be used for detection. However, analyzing raw DNS data is complex and computationally.
An autoencoder is used to transform dynamic DNS traffic data into lower-dimensional vectors called DNS profiles, efficiently capturing the characteristics of the traffic.
- Classification: Identifies malicious domains based on their profiles.
- Clustering: Groups malicious domains with similar traffic patterns, revealing attack types (e.g., DDNS, tunneling, heartbeats).
- Anomaly Detection: Identifies unusual traffic patterns that may indicate malicious activity or unintentional issues.
Problem: Malicious DNS traffic often exhibits unique patterns that can be used for detection. However, analyzing raw DNS data is complex and computationally.
An autoencoder is used to transform dynamic DNS traffic data into lower-dimensional vectors called DNS profiles, efficiently capturing the characteristics of the traffic.
- Classification: Identifies malicious domains based on their profiles.
- Clustering: Groups malicious domains with similar traffic patterns, revealing attack types (e.g., DDNS, tunneling, heartbeats).
- Anomaly Detection: Identifies unusual traffic patterns that may indicate malicious activity or unintentional issues.
DeepMind's Gemma Scope provides researchers with tools to better understand how Gemma 2 language models work through a collection of sparse autoencoders. This helps in understanding the inner workings of these models and addressing concerns like hallucinations and potential manipulation.
