Machine learning–(ML) based approach is considered as one of the most promising techniques for Android malware detection and has achieved high accuracy by leveraging commonly used features. In practice, ML classifications only provide a binary label to mobile users app security analysts. However, stakeholders are more interested in reason why apps classified malicious both academia industry. Th...

Classified ad sites routinely process hundreds of thousands to millions of posted ads, and only a small percentage of those may be fraudulent. Online scammers often go through a great amount of effort to make their listings look legitimate. Examples include copying existing advertisements from other services, tunneling through local proxies, and even paying for extra services using stolen accou...

We study Differential Privacy in the abstract setting of Probability on metric spaces. Numerical, categorical and functional data can be handled in a uniform manner in this setting. We demonstrate how mechanisms based on data sanitisation and those that rely on adding noise to query responses fit within this framework. We prove that once the sanitisation is differentially private, then so is th...

Using pervasive provenance to secure mainstream systems has recently attracted interest from industry and government. Recording, storing and managing all of the provenance associated with a system is a considerable challenge. Analyzing the resulting noisy, heterogeneous, continuously-growing provenance graph adds to this challenge, and apparently necessitates segmentation, that is, approximatin...