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Found 5 papers, 2 papers with code
Targeted and Troublesome: Tracking and Advertising on Children's Websites
To address this gap, we present a measurement of tracking and (targeted) advertising on websites directed at children.
Level Up with RealAEs: Leveraging Domain Constraints in Feature Space to Strengthen Robustness of Android Malware Detection
Realistic attacks in the Android malware domain create Realizable Adversarial Examples (RealAEs), i. e., AEs that satisfy the domain constraints of Android malware.
EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detection
The proposed manipulation technique is a query-efficient optimization algorithm that can find and inject optimal sequences of transformations into malware apps.
Where's Crypto?: Automated Identification and Classification of Proprietary Cryptographic Primitives in Binary Code
The continuing use of proprietary cryptography in embedded systems across many industry verticals, from physical access control systems and telecommunications to machine-to-machine authentication, presents a significant obstacle to black-box security-evaluation efforts.
Cryptography and Security 68M25 E.3
Less is More: A privacy-respecting Android malware classifier using Federated Learning
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 manner.
