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

Anomaly Detection via Federated Learning

no code implementations • 12 Oct 2022 • Marc Vucovich, Amogh Tarcar, Penjo Rebelo, Narendra Gade, Ruchi Porwal, Abdul Rahman, Christopher Redino, Kevin Choi, Dhruv Nandakumar, Robert Schiller, Edward Bowen, Alex West, Sanmitra Bhattacharya, Balaji Veeramani

Machine learning has helped advance the field of anomaly detection by incorporating classifiers and autoencoders to decipher between normal and anomalous behavior.

Anomaly Detection Federated Learning +1

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