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Deceptive patterns in digital interfaces manipulate users into making unintended decisions, exploiting cognitive biases and psychological vulnerabilities. These patterns have become ubiquitous on various digital platforms. While efforts to…
In this position paper we advocate software model checking as a technique suitable for security analysis of mobile apps. Our recommendation is based on promising results that we achieved on analysing app collusion in the context of the…
Android is the most widely used smartphone OS with 82.8% market share in 2015. It is therefore the most widely targeted system by malware authors. Researchers rely on dynamic analysis to extract malware behaviors and often use emulators to…
Non-functional properties, such as runtime or memory use, are important to mobile app users and developers, as they affect user experience. Previous work on automated improvement of non-functional properties in mobile apps failed to address…
Digital lending applications, commonly referred to as loan apps, have become a primary channel for microcredit in emerging markets. However, many of these apps demand excessive permissions and misuse sensitive user data for coercive…
Due to Android's open source feature and low barriers to entry for developers, millions of developers and third-party organizations have been attracted into the Android ecosystem. However, over 90 percent of mobile malware are found…
On the modern web, trackers and advertisers frequently construct and monetize users' detailed behavioral profiles without consent. Despite various studies on web tracking mechanisms and advertisements, there has been no rigorous study…
Permission analysis is a widely used method for Android malware detection. It involves examining the permissions requested by an application to access sensitive data or perform potentially malicious actions. In recent years, various machine…
Despite the widespread use of encryption techniques to provide confidentiality over Internet communications, mobile device users are still susceptible to privacy and security risks. In this paper, a new Deep Neural Network (DNN) based user…
The Android mining sandbox approach consists in running dynamic analysis tools on a benign version of an Android app and recording every call to sensitive APIs. Later, one can use this information to (a) prevent calls to other sensitive…
Android is the most popular mobile operating system in the world, running on more than 70% of mobile devices. This implies a gigantic and very competitive market for Android apps. Being successful in such a market is far from trivial and…
Unlike the Web where each web page has a global URL to reach, a specific "content page" inside a mobile app cannot be opened unless the user explores the app with several operations from the landing page. Recently, deep links have been…
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…
While computer systems that allow users to interact through conversational natural language (i.e., chatbots) have existed for many years, varying types of applications advertising AI companionship (e.g., Character AI, Replika) have…
Despite over 3.5 million Android apps and 200+ million Android Auto-compatible vehicles, only a few hundred apps support Android Auto due to platform-specific compliance requirements. Android Auto mandates service-based architectures in…
Desktop browser extensions have long allowed users to improve their experience online and tackle widespread harms on websites. So far, no equivalent solution exists for mobile apps, despite the fact that individuals now spend significantly…
With the rapid technological advancement, security has become a major issue due to the increase in malware activity that poses a serious threat to the security and safety of both computer systems and stakeholders. To maintain stakeholders,…
The rapid growth of online advertising has fueled the growth of ad-blocking software, such as new ad-blocking and privacy-oriented browsers or browser extensions. In response, both ad publishers and ad networks are constantly trying to…
The deep learning approach to detecting malicious software (malware) is promising but has yet to tackle the problem of dataset shift, namely that the joint distribution of examples and their labels associated with the test set is different…
The rapid growth of mobile applications has escalated Android malware threats. Although there are numerous detection methods, they often struggle with evolving attacks, dataset biases, and limited explainability. Large Language Models…