Poisoning Network Flow Classifiers

no code implementations • 2 Jun 2023 • Giorgio Severi, Simona Boboila, Alina Oprea, John Holodnak, Kendra Kratkiewicz, Jason Matterer

As machine learning (ML) classifiers increasingly oversee the automated monitoring of network traffic, studying their resilience against adversarial attacks becomes critical.

Backdoor Attacks in Peer-to-Peer Federated Learning

no code implementations • 23 Jan 2023 • Gokberk Yar, Simona Boboila, Cristina Nita-Rotaru, Alina Oprea

Most machine learning applications rely on centralized learning processes, opening up the risk of exposure of their training datasets.

Backdoor Attack Federated Learning

CELEST: Federated Learning for Globally Coordinated Threat Detection

no code implementations • 23 May 2022 • Talha Ongun, Simona Boboila, Alina Oprea, Tina Eliassi-Rad, Jason Hiser, Jack Davidson

In this study, we propose CELEST (CollaborativE LEarning for Scalable Threat detection, a federated machine learning framework for global threat detection over HTTP, which is one of the most commonly used protocols for malware dissemination and communication.

Active Learning Federated Learning

On Designing Machine Learning Models for Malicious Network Traffic Classification

no code implementations • 10 Jul 2019 • Talha Ongun, Timothy Sakharaov, Simona Boboila, Alina Oprea, Tina Eliassi-Rad

Machine learning (ML) started to become widely deployed in cyber security settings for shortening the detection cycle of cyber attacks.

BIG-bench Machine Learning Classification +2