18 citations · 44 across the 14 of their papers we have counts for
14 papers
BlueSTAR: Tiered Agentic Architecture for Autonomous Cyber Defense
Simona Boboila, Xavier Cadet, Edward Koh +4
Cyber attacks are increasingly automated, narrowing the time available for human analysts to detect, reason about, and respond to intrusions. Large language models (LLMs) offer a p…
Retrieval-Augmented LLMs for Security Incident Analysis
Xavier Cadet, Aditya Vikram Singh, Harsh Mamania +6
Investigating cybersecurity incidents requires collecting and analyzing evidence from multiple log sources, including intrusion detection alerts, network traffic records, and authe…
PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense
Xavier Cadet, Simona Boboila, Sie Hendrata Dharmawan +2
Cyber defense requires automating defensive decision-making under stealthy, deceptive, and continuously evolving adversarial strategies. The FlipIt game provides a foundational fra…
Quantitative Resilience Modeling for Autonomous Cyber Defense
Xavier Cadet, Simona Boboila, Edward Koh +2
Cyber resilience is the ability of a system to recover from an attack with minimal impact on system operations. However, characterizing a network's resilience under a cyber attack…
Hierarchical Multi-agent Reinforcement Learning for Cyber Network Defense
Aditya Vikram Singh, Ethan Rathbun, Emma Graham +4
Recent advances in multi-agent reinforcement learning (MARL) have created opportunities to solve complex real-world tasks. Cybersecurity is a notable application area, where defend…
Model-agnostic clean-label backdoor mitigation in cybersecurity environments
Giorgio Severi, Simona Boboila, John Holodnak +4
The training phase of machine learning models is a delicate step, especially in cybersecurity contexts. Recent research has surfaced a series of insidious training-time attacks tha…
Poisoning Network Flow Classifiers
Giorgio Severi, Simona Boboila, Alina Oprea +3
As machine learning (ML) classifiers increasingly oversee the automated monitoring of network traffic, studying their resilience against adversarial attacks becomes critical. This…
Backdoor Attacks in Peer-to-Peer Federated Learning
Georgios Syros, Gokberk Yar, Simona Boboila +2
Most machine learning applications rely on centralized learning processes, opening up the risk of exposure of their training datasets. While federated learning (FL) mitigates to so…
Modeling Self-Propagating Malware with Epidemiological Models
Alesia Chernikova, Nicolò Gozzi, Simona Boboila +3
Self-propagating malware (SPM) has recently resulted in large financial losses and high social impact, with well-known campaigns such as WannaCry and Colonial Pipeline being able t…
Cyber Network Resilience against Self-Propagating Malware Attacks
Alesia Chernikova, Nicolò Gozzi, Simona Boboila +6
Self-propagating malware (SPM) has led to huge financial losses, major data breaches, and widespread service disruptions in recent years. In this paper, we explore the problem of d…
CELEST: Federated Learning for Globally Coordinated Threat Detection
Talha Ongun, Simona Boboila, Alina Oprea +3
The cyber-threat landscape has evolved tremendously in recent years, with new threat variants emerging daily, and large-scale coordinated campaigns becoming more prevalent. In this…
PORTFILER: Port-Level Network Profiling for Self-Propagating Malware Detection
Talha Ongun, Oliver Spohngellert, Benjamin Miller +7
Recent self-propagating malware (SPM) campaigns compromised hundred of thousands of victim machines on the Internet. It is challenging to detect these attacks in their early stages…
Collaborative Information Sharing for ML-Based Threat Detection
Talha Ongun, Simona Boboila, Alina Oprea +4
Recently, coordinated attack campaigns started to become more widespread on the Internet. In May 2017, WannaCry infected more than 300,000 machines in 150 countries in a few days a…
On Designing Machine Learning Models for Malicious Network Traffic Classification
Talha Ongun, Timothy Sakharaov, Simona Boboila +2
Machine learning (ML) started to become widely deployed in cyber security settings for shortening the detection cycle of cyber attacks. To date, most ML-based systems are either pr…