18 citations · 44 across the 14 of their papers we have counts for
11 papers · 1 filter
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…
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…
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…
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…