Zero Day Threat Detection Using Metric Learning Autoencoders

no code implementations • 1 Nov 2022 • Dhruv Nandakumar, Robert Schiller, Christopher Redino, Kevin Choi, Abdul Rahman, Edward Bowen, Marc Vucovich, Joe Nehila, Matthew Weeks, Aaron Shaha

The proliferation of zero-day threats (ZDTs) to companies' networks has been immensely costly and requires novel methods to scan traffic for malicious behavior at massive scale.

Metric Learning

Lateral Movement Detection Using User Behavioral Analysis

no code implementations • 29 Aug 2022 • Deepak Kushwaha, Dhruv Nandakumar, Akshay Kakkar, Sanvi Gupta, Kevin Choi, Christopher Redino, Abdul Rahman, Sabthagiri Saravanan Chandramohan, Edward Bowen, Matthew Weeks, Aaron Shaha, Joe Nehila

Lateral Movement refers to methods by which threat actors gain initial access to a network and then progressively move through said network collecting key data about assets until they reach the ultimate target of their attack.

Feature Engineering

Zero Day Threat Detection Using Graph and Flow Based Security Telemetry

no code implementations • 4 May 2022 • Christopher Redino, Dhruv Nandakumar, Robert Schiller, Kevin Choi, Abdul Rahman, Edward Bowen, Matthew Weeks, Aaron Shaha, Joe Nehila

With this paper, the authors' overarching goal is to provide a novel architecture and training methodology for cyber anomaly detectors that can generalize to multiple IT networks with minimal to no retraining while still maintaining strong performance.

Novelty Detection