Corelight Labs

Research

From detecting attacks to profiling behavior, Corelight Labs creates new ways to deepen network insight and strengthen enterprise security. We work in close partnership with other innovators at Corelight, and we take pride in the robust, deeply technical capabilities we create.

Recent research

Network Detection of Interactive SSH Impostors Using Deep Learning

Impostors who have stolen a user's SSH login credentials can inflict significant harm to the systems to which the user has remote access. We consider the problem of identifying such imposters when they conduct interactive SSH logins by detecting discrepancies in the timing and sizes of the client-side data packets, which generally reflect the typing dynamics of the person sending keystrokes over the connection.

Recent research

GGFAST: Automating Generation of Flexible Network Traffic Classifiers

When employing supervised machine learning to analyze network traffic, the heart of the task often lies in developing effective features for the ML to leverage. We develop GGFAST, a unified, automated framework that can build powerful classifiers for specific network traffic analysis tasks, built on interpretable features. The framework uses only packet sizes, directionality, and sequencing, facilitating analysis in a payload-agnostic fashion that remains applicable in the presence of encryption.