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Mobile apps have become essential of our daily lives, making code quality a critical concern for developers. Behavioural code smells are characteristics in the source code that induce inappropriate code behaviour during execution, which…
Machine learning methods can detect Android malware with very high accuracy. However, these classifiers have an Achilles heel, concept drift: they rapidly become out of date and ineffective, due to the evolution of malware apps and benign…
In mobile telecommunications, alarms act as early warning signals. They are triggered when a cell, the basic unit of radio coverage, shuts down or behaves abnormally. This signals a degradation in service quality, which directly affects the…
Advanced Persistent Threats (APTs) are sophisticated, targeted cyberattacks designed to gain unauthorized access to systems and remain undetected for extended periods. To evade detection, APT cyberattacks deceive defense layers with…
Application Programming Interfaces (APIs) often have usage constraints, such as restrictions on call order or call conditions. API misuses, i.e., violations of these constraints, may lead to software crashes, bugs, and vulnerabilities.…
To identify security vulnerabilities in Android applications, numerous static application security testing (SAST) tools have been proposed. However, it poses significant challenges to assess their overall performance on diverse…
Filesystem vulnerabilities persist as a significant threat to Android systems, despite various proposed defenses and testing techniques. The complexity of program behaviors and access control mechanisms in Android systems makes it…
Mobile application developers are required to disclose how they collect, use, and share user data in compliance with privacy regulations. To support transparency, major app marketplaces have introduced standardized disclosure mechanisms. In…
Machine learning based solutions have been successfully employed for automatic detection of malware on Android. However, machine learning models lack robustness to adversarial examples, which are crafted by adding carefully chosen…
Despite its widespread use in Android apps, reflection poses graving problems for static security analysis. Currently, string inference is applied to handle reflection, resulting in significantly missed security vulnerabilities. In this…
Multi-channel keyword spotting (KWS) has become crucial for voice-based applications in edge environments. However, its substantial computational and energy requirements pose significant challenges. We introduce ASAP-FE (Agile…
The Android OS has become the most popular mobile operating system leading to a significant increase in the spread of Android malware. Consequently, several static and dynamic analysis systems have been developed to detect Android malware.…
In present study, in order to improve the performance and reduce the amount of power which is dissipated in heterogeneous multicore processors, the ability of detecting the program execution phases is investigated. The programs execution…
Security of mobile apps affects the security of their users. This has fueled the development of techniques to automatically detect vulnerabilities in mobile apps and help developers secure their apps; specifically, in the context of Android…
Following the increasing popularity of mobile ecosystems, cybercriminals have increasingly targeted them, designing and distributing malicious apps that steal information or cause harm to the device's owner. Aiming to counter them,…
The login functionality, being the gateway to app usage, plays a critical role in both user experience and application security. As Android apps increasingly incorporate login functionalities, they support a variety of authentication…
Bugs that surface in mobile applications can be difficult to reproduce and fix due to several confounding factors including the highly GUI-driven nature of mobile apps, varying contextual states, differing platform versions and device…
Ethereum smart contracts are an innovation built on top of the blockchain technology, which provides a platform for automatically executing contracts in an anonymous, distributed, and trusted way. The problem is magnified by the fact that…
State-of-the-art machine-learning methods for event cameras treat events as dense representations and process them with conventional deep neural networks. Thus, they fail to maintain the sparsity and asynchronous nature of event data,…
The growing demand for real-time DNN applications on edge devices necessitates faster inference of increasingly complex models. Although many devices include specialized accelerators (e.g., mobile GPUs), dynamic control-flow operators and…