相关论文: A Systematic Assessment on Android Third-party Lib…

Android applications are frequently plagiarized or repackaged, and software obfuscation is a recommended protection against these practices. However, there is very little data on the overall rates of app obfuscation, the techniques used, or…

New operating systems for mobile devices allow their users to download millions of applications created by various individual programmers, some of which may be malicious or flawed. In order to detect that an application is malicious,…

Google's Android is a comprehensive software framework for mobile communication devices (i.e., smartphones, PDAs). The Android framework includes an operating system, middleware and a set of key applications. The incorporation of integrated…

This paper reviews work published between 2002 and 2022 in the fields of Android malware, clone, and similarity detection. It examines the data sources, tools, and features used in existing research and identifies the need for a…

Software libraries play a critical role in the functionality, efficiency, and maintainability of software systems. As developers increasingly rely on Large Language Models (LLMs) to streamline their coding processes, the effectiveness of…

Third-Party Library (TPL) detection, which identifies reused libraries in binary code, is critical for software security analysis. At its core, TPL detection depends on binary decomposition-the process of partitioning a monolithic binary…

Malware detection is a growing problem particularly on the Android mobile platform due to its increasing popularity and accessibility to numerous third party app markets. This has also been made worse by the increasingly sophisticated…

Mobile apps are used in a variety of health settings, from apps that help providers, to apps designed for patients, to health and fitness apps designed for the general public. These apps ask the user for, and then collect and leak a wealth…

Privacy concerns have long been expressed around smart devices, and the concerns around Android apps have been studied by many past works. Over the past 10 years, we have crawled and scraped data for almost 1.9 million apps, and also stored…

Recommender systems for software engineering (RSSE) play a crucial role in automating development tasks by providing relevant suggestions according to the developer's context. However, they suffer from the so-called popularity bias, i.e.,…

Android filesystem access control provides a foundation for Android system integrity. Android utilizes a combination of mandatory (e.g., SEAndroid) and discretionary (e.g., UNIX permissions) access control, both to protect the Android…

Following OpenAI's introduction of GPTs, a surge in GPT apps has led to the launch of dedicated LLM app stores. Nevertheless, given its debut, there is a lack of sufficient understanding of this new ecosystem. To fill this gap, this paper…

Software libraries are central to the functionality, security, and maintainability of modern code. As developers increasingly turn to Large Language Models (LLMs) to assist with programming tasks, understanding how these models recommend…

Smartphone apps usually have access to sensitive user data such as contacts, geo-location, and account credentials and they might share such data to external entities through the Internet or with other apps. Confidentiality of user data…

Android applications collecting data from users must protect it according to the current legal frameworks. Such data protection has become even more important since the European Union rolled out the General Data Protection Regulation…

Machine learning (ML) has gained significant adoption in Android malware detection to address the escalating threats posed by the rapid proliferation of malware attacks. However, recent studies have revealed the inherent vulnerabilities of…

The impressive growth of smartphone devices in combination with the rising ubiquity of using mobile platforms for sensitive applications such as Internet banking, have triggered a rapid increase in mobile malware. In recent literature, many…

Large language model (LLM) services have recently begun offering a plugin ecosystem to interact with third-party API services. This innovation enhances the capabilities of LLMs, but it also introduces risks, as these plugins developed by…

This study examines machine learning techniques like Decision Trees, Support Vector Machines, Logistic Regression, Neural Networks, and ensemble methods to detect Android malware. The study evaluates these models on a dataset of Android…

Deep learning (DL) techniques are on the rise in the software engineering research community. More and more approaches have been developed on top of DL models, also due to the unprecedented amount of software-related data that can be used…