242 citations · 349 across the 13 of their papers we have counts for
14 papers
FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning
Hossein Fereidooni, Alessandro Pegoraro, Phillip Rieger +2
Federated learning (FL) is a collaborative learning paradigm allowing multiple clients to jointly train a model without sharing their training data. However, FL is susceptible to p…
ClearMark: Intuitive and Robust Model Watermarking via Transposed Model Training
Torsten Krauß, Jasper Stang, Alexandra Dmitrienko
Due to costly efforts during data acquisition and model training, Deep Neural Networks (DNNs) belong to the intellectual property of the model creator. Hence, unauthorized use, the…
TorMult: Introducing a Novel Tor Bandwidth Inflation Attack
Christoph Sendner, Jasper Stang, Alexandra Dmitrienko +2
The Tor network is the most prominent system for providing anonymous communication to web users, with a daily user base of 2 million users. However, since its inception, it has bee…
G-Scan: Graph Neural Networks for Line-Level Vulnerability Identification in Smart Contracts
Christoph Sendner, Ruisi Zhang, Alexander Hefter +2
Due to the immutable and decentralized nature of Ethereum (ETH) platform, smart contracts are prone to security risks that can result in financial loss. While existing machine lear…
Metadata-based Malware Detection on Android using Machine Learning
Alexander Hefter, Christoph Sendner, Alexandra Dmitrienko
In the digitized world, smartphones and their apps play an important role. To name just a few examples, some apps offer possibilities for entertainment, others for online banking,…
Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigations
Torsten Krauß, Alexandra Dmitrienko
Federated Learning (FL) facilitates decentralized machine learning model training, preserving data privacy, lowering communication costs, and boosting model performance through div…