Automatic malware classifiers often perform badly on the detection of new malware, ie, their
robustness is poor. We study the machine-learning-based mobile malware classifiers and …

Measuring and understanding the performance of mobile networks is becoming very
important for end users and operators. Despite the availability of many measurement apps …

Android has become one of the most popular operating systems for mobile devices. As the
number of applications for the Android ecosystem grows, so is their complexity, increasing …

Current machine-learning-based malware detection seldom provides information about why
an app is considered bad. We study the automatic explanation of unwanted behaviours in …

Policy enforcers are sophisticated runtime components that can prevent failures by enforcing
the correct behavior of the software. While a single enforcer can be easily designed focusing …

Using network measurement apps has become a very effective approach to crowdsourcing
WiFi network performance data. However, these apps usually measure the user-level …

A faithful characterization of backdoors is a prerequisite for an effective automated detection.
Unfortunately, as we demonstrate, formalization attempts in terms of temporal safety …

Abstract Machine-learning-based Android malware classifiers perform badly on the
detection of new malware, in particular, when they take API calls and permissions as input …

Android smartphones have become a vital component of the daily routine of millions of
people, running a plethora of applications available in the official and alternative …

Runtime verification (RV) covering the whole Android system is challenging, due to the lack
of support for analyzing and monitoring events across multiple processes. Existing RV …