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20162025
most citedSystematic Classification of Side-Channel Attacks: A Case Study for Mobile Devices

241 citations · 338 across the 13 of their papers we have counts for

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Showing cs.CRShow all

8 papers · 1 filter

cs.CR2024

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…

cs.CR2024

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…

cs.CR2024★ 7 cited

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…

cs.CR2021★ 2 cited

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…

cs.CR2020★ 2 cited

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…

cs.CR2020

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…

cs.CR2016★ 241 cited

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…

cs.CR2016

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…