241 citations · 338 across the 13 of their papers we have counts for
14 papers
"Sorry for bugging you so much." Exploring Developers' Behavior Towards Privacy-Compliant Implementation
Stefan Albert Horstmann, Sandy Hong, David Klein +5
While protecting user data is essential, software developers often fail to fulfill privacy requirements. However, the reasons why they struggle with privacy-compliant implementatio…
Vulnerability, Where Art Thou? An Investigation of Vulnerability Management in Android Smartphone Chipsets
Daniel Klischies, Philipp Mackensen, Veelasha Moonsamy
Vulnerabilities in Android smartphone chipsets have severe consequences, as recent real-world attacks have demonstrated that adversaries can leverage vulnerabilities to execute arb…
On the Effectiveness of Adversarial Training on Malware Classifiers
Hamid Bostani, Jacopo Cortellazzi, Daniel Arp +3
Adversarial Training (AT) is a key defense against Machine Learning evasion attacks, but its effectiveness for real-world malware detection remains poorly understood. This uncertai…
Improving Adversarial Robustness in Android Malware Detection by Reducing the Impact of Spurious Correlations
Hamid Bostani, Zhengyu Zhao, Veelasha Moonsamy
Machine learning (ML) has demonstrated significant advancements in Android malware detection (AMD); however, the resilience of ML against realistic evasion attacks remains a major…
Spatial-Domain Wireless Jamming with Reconfigurable Intelligent Surfaces
Philipp Mackensen, Paul Staat, Stefan Roth +3
Wireless communication infrastructure is a cornerstone of modern digital society, yet it remains vulnerable to the persistent threat of wireless jamming. Attackers can easily creat…
Targeted and Troublesome: Tracking and Advertising on Children's Websites
Zahra Moti, Asuman Senol, Hamid Bostani +4
On the modern web, trackers and advertisers frequently construct and monetize users' detailed behavioral profiles without consent. Despite various studies on web tracking mechanism…
Level Up with ML Vulnerability Identification: Leveraging Domain Constraints in Feature Space for Robust Android Malware Detection
Hamid Bostani, Zhengyu Zhao, Zhuoran Liu +1
Machine Learning (ML) promises to enhance the efficacy of Android Malware Detection (AMD); however, ML models are vulnerable to realistic evasion attacks--crafting realizable Adver…
IRShield: A Countermeasure Against Adversarial Physical-Layer Wireless Sensing
Paul Staat, Simon Mulzer, Stefan Roth +5
Wireless radio channels are known to contain information about the surrounding propagation environment, which can be extracted using established wireless sensing methods. Thus, tod…
EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detection
Hamid Bostani, Veelasha Moonsamy
Over the last decade, researchers have extensively explored the vulnerabilities of Android malware detectors to adversarial examples through the development of evasion attacks; how…
Where's Crypto?: Automated Identification and Classification of Proprietary Cryptographic Primitives in Binary Code
Carlo Meijer, Veelasha Moonsamy, Jos Wetzels
The continuing use of proprietary cryptography in embedded systems across many industry verticals, from physical access control systems and telecommunications to machine-to-machine…
Less is More: A privacy-respecting Android malware classifier using Federated Learning
Rafa Gálvez, Veelasha Moonsamy, Claudia Diaz
In this paper we present LiM ("Less is More"), a malware classification framework that leverages Federated Learning to detect and classify malicious apps in a privacy-respecting ma…
Malicious cryptocurrency miners: Status and Outlook
Radhesh Krishnan Konoth, Rolf van Wegberg, Veelasha Moonsamy +1
In this study, we examine the behavior and profitability of modern malware that mines cryptocurrency. Unlike previous studies, we look at the cryptocurrency market as a whole, rath…
Systematic Classification of Side-Channel Attacks: A Case Study for Mobile Devices
Raphael Spreitzer, Veelasha Moonsamy, Thomas Korak +1
Side-channel attacks on mobile devices have gained increasing attention since their introduction in 2007. While traditional side-channel attacks, such as power analysis attacks and…
No Free Charge Theorem: a Covert Channel via USB Charging Cable on Mobile Devices
Riccardo Spolaor, Laila Abudahi, Veelasha Moonsamy +2
More and more people are regularly using mobile and battery-powered handsets, such as smartphones and tablets. At the same time, thanks to the technological innovation and to the h…