669 citations · 778 across the 10 of their papers we have counts for
11 papers
Real-time Adversarial Perturbations against Deep Reinforcement Learning Policies: Attacks and Defenses
Buse G. A. Tekgul, Shelly Wang, Samuel Marchal +1
Deep reinforcement learning (DRL) is vulnerable to adversarial perturbations. Adversaries can mislead the policies of DRL agents by perturbing the state of the environment observed…
FLAME: Taming Backdoors in Federated Learning (Extended Version 1)
Thien Duc Nguyen, Phillip Rieger, Huili Chen +10
Federated Learning (FL) is a collaborative machine learning approach allowing participants to jointly train a model without having to share their private, potentially sensitive loc…
WAFFLE: Watermarking in Federated Learning
Buse Gul Atli, Yuxi Xia, Samuel Marchal +1
Federated learning is a distributed learning technique where machine learning models are trained on client devices in which the local training data resides. The training is coordin…
Detecting organized eCommerce fraud using scalable categorical clustering
Samuel Marchal, Sebastian Szyller
Online retail, eCommerce, frequently falls victim to fraud conducted by malicious customers (fraudsters) who obtain goods or services through deception. Fraud coordinated by groups…
Extraction of Complex DNN Models: Real Threat or Boogeyman?
Buse Gul Atli, Sebastian Szyller, Mika Juuti +2
Recently, machine learning (ML) has introduced advanced solutions to many domains. Since ML models provide business advantage to model owners, protecting intellectual property of M…
DAWN: Dynamic Adversarial Watermarking of Neural Networks
Sebastian Szyller, Buse Gul Atli, Samuel Marchal +1
Training machine learning (ML) models is expensive in terms of computational power, amounts of labeled data and human expertise. Thus, ML models constitute intellectual property (I…
PRADA: Protecting against DNN Model Stealing Attacks
Mika Juuti, Sebastian Szyller, Samuel Marchal +1
Machine learning (ML) applications are increasingly prevalent. Protecting the confidentiality of ML models becomes paramount for two reasons: (a) a model can be a business advantag…
DÏoT: A Federated Self-learning Anomaly Detection System for IoT
Thien Duc Nguyen, Samuel Marchal, Markus Miettinen +3
IoT devices are increasingly deployed in daily life. Many of these devices are, however, vulnerable due to insecure design, implementation, and configuration. As a result, many net…
Profiling Users by Modeling Web Transactions
Radek Tomsu, Samuel Marchal, N. Asokan
Users of electronic devices, e.g., laptop, smartphone, etc. have characteristic behaviors while surfing the Web. Profiling this behavior can help identify the person using a given…
IoT Sentinel: Automated Device-Type Identification for Security Enforcement in IoT
Markus Miettinen, Samuel Marchal, Ibbad Hafeez +3
With the rapid growth of the Internet-of-Things (IoT), concerns about the security of IoT devices have become prominent. Several vendors are producing IP-connected devices for home…
Know Your Phish: Novel Techniques for Detecting Phishing Sites and their Targets
Samuel Marchal, Kalle Saari, Nidhi Singh +1
Phishing is a major problem on the Web. Despite the significant attention it has received over the years, there has been no definitive solution. While the state-of-the-art solution…