273 citations · 914 across the 36 of their papers we have counts for
25 papers · 1 filter
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…
Bad Citrus: Reducing Adversarial Costs with Model Distances
Giorgio Severi, Will Pearce, Alina Oprea
Recent work by Jia et al., showed the possibility of effectively computing pairwise model distances in weight space, using a model explanation technique known as LIME. This method…
Network-Level Adversaries in Federated Learning
Giorgio Severi, Matthew Jagielski, Gökberk Yar +3
Federated learning is a popular strategy for training models on distributed, sensitive data, while preserving data privacy. Prior work identified a range of security threats on fed…
Black-box Attacks Against Neural Binary Function Detection
Joshua Bundt, Michael Davinroy, Ioannis Agadakos +2
Binary analyses based on deep neural networks (DNNs), or neural binary analyses (NBAs), have become a hotly researched topic in recent years. DNNs have been wildly successful at pu…
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…
SafeNet: The Unreasonable Effectiveness of Ensembles in Private Collaborative Learning
Harsh Chaudhari, Matthew Jagielski, Alina Oprea
Secure multiparty computation (MPC) has been proposed to allow multiple mutually distrustful data owners to jointly train machine learning (ML) models on their combined data. Howev…
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…
Living-Off-The-Land Command Detection Using Active Learning
Talha Ongun, Jack W. Stokes, Jonathan Bar Or +6
In recent years, enterprises have been targeted by advanced adversaries who leverage creative ways to infiltrate their systems and move laterally to gain access to critical data. O…
Adversarial Robustness Verification and Attack Synthesis in Stochastic Systems
Lisa Oakley, Alina Oprea, Stavros Tripakis
Probabilistic model checking is a useful technique for specifying and verifying properties of stochastic systems including randomized protocols and reinforcement learning models. E…
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 Generating and Labeling Network Traffic with Realistic, Self-Propagating Malware
Molly Buchanan, Jeffrey W. Collyer, Jack W. Davidson +6
Research and development of techniques which detect or remediate malicious network activity require access to diverse, realistic, contemporary data sets containing labeled maliciou…
Extracting Training Data from Large Language Models
Nicholas Carlini, Florian Tramer, Eric Wallace +9
It has become common to publish large (billion parameter) language models that have been trained on private datasets. This paper demonstrates that in such settings, an adversary ca…
Auditing Differentially Private Machine Learning: How Private is Private SGD?
Matthew Jagielski, Jonathan Ullman, Alina Oprea
We investigate whether Differentially Private SGD offers better privacy in practice than what is guaranteed by its state-of-the-art analysis. We do so via novel data poisoning atta…
With Great Dispersion Comes Greater Resilience: Efficient Poisoning Attacks and Defenses for Linear Regression Models
Jialin Wen, Benjamin Zi Hao Zhao, Minhui Xue +2
With the rise of third parties in the machine learning pipeline, the service provider in "Machine Learning as a Service" (MLaaS), or external data contributors in online learning,…
Explanation-Guided Backdoor Poisoning Attacks Against Malware Classifiers
Giorgio Severi, Jim Meyer, Scott Coull +1
Training pipelines for machine learning (ML) based malware classification often rely on crowdsourced threat feeds, exposing a natural attack injection point. In this paper, we stud…
FENCE: Feasible Evasion Attacks on Neural Networks in Constrained Environments
Alesia Chernikova, Alina Oprea
As advances in Deep Neural Networks (DNNs) demonstrate unprecedented levels of performance in many critical applications, their vulnerability to attacks is still an open question.…
AppMine: Behavioral Analytics for Web Application Vulnerability Detection
Indranil Jana, Alina Oprea
Web applications in widespread use have always been the target of large-scale attacks, leading to massive disruption of services and financial loss, as in the Equifax data breach.…
The House That Knows You: User Authentication Based on IoT Data
Talha Ongun, Oliver Spohngellert, Alina Oprea +3
Home-based Internet of Things (IoT) devices have gained in popularity and many households have become 'smart' by using devices such as smart sensors, locks, and voice-based assista…
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…
QFlip: An Adaptive Reinforcement Learning Strategy for the FlipIt Security Game
Lisa Oakley, Alina Oprea
A rise in Advanced Persistent Threats (APTs) has introduced a need for robustness against long-running, stealthy attacks which circumvent existing cryptographic security guarantees…
Private Hierarchical Clustering and Efficient Approximation
Xianrui Meng, Dimitrios Papadopoulos, Alina Oprea +1
In collaborative learning, multiple parties contribute their datasets to jointly deduce global machine learning models for numerous predictive tasks. Despite its efficacy, this lea…
Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning
Matthew Jagielski, Alina Oprea, Battista Biggio +3
As machine learning becomes widely used for automated decisions, attackers have strong incentives to manipulate the results and models generated by machine learning algorithms. In…