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Foivos Timotheos Proestakis

Publications and source records attributed to Foivos Timotheos Proestakis.

2 recordsLinked to original sources

An Empirical Comparison of Security and Privacy Characteristics of Android Messaging Apps

Mobile messaging apps are a fundamental communication infrastructure, used by billions of people every day to share information, including sensitive data. Security and Privacy are thus critical concerns for such applications. Although the cryptographic protocols prevalent in messaging apps are generally well studied, other relevant implementation characteristics of such apps, such as their software architecture, permission use, and network-related runtime behavior, have not received enough attention. In this paper, we present a methodology for comparing implementation characteristics of messaging applications by employing static and dynamic analysis under reproducible scenarios to identify discrepancies with potential security and privacy implications. We apply this methodology to study the Android clients of the Meta Messenger, Signal, and Telegram apps. Our main findings reveal discrepancies in application complexity, attack surface, and network behavior. Statically, Messenger presents the largest attack surface and the highest number of static analysis warnings, while Telegram requests the most dangerous permissions. In contrast, Signal consistently demonstrates a minimalist design with the fewest dependencies and dangerous permissions. Dynamically, these differences are reflected in network activity; Messenger is by far the most active, exhibiting persistent background communication, whereas Signal is the least active. Furthermore, our analysis shows that all applications properly adhere to the Android permission model, with no evidence of unauthorized data access.

cs.CR

PyTrim: A Practical Tool for Reducing Python Dependency Bloat

Dependency bloat is a persistent challenge in Python projects, which increases maintenance costs and security risks. While numerous tools exist for detecting unused dependencies in Python, removing these dependencies across the source code and configuration files of a project requires manual effort and expertise. To tackle this challenge we introduce PYTRIM, an end-to-end system to automate this process. PYTRIM eliminates unused imports and package declarations across a variety of file types, including Python source and configuration files such as requirements.txt and setup.py. PYTRIM's modular design makes it agnostic to the source of dependency bloat information, enabling integration with any detection tool. Beyond its contribution when it comes to automation, PYTRIM also incorporates a novel dynamic analysis component that improves dependency detection recall. Our evaluation of PYTRIM's end-to-end effectiveness on a ground-truth dataset of 37 merged pull requests from prior work, shows that PYTRIM achieves 98.3% accuracy in replicating human-made changes. To show its practical impact, we run PYTRIM on 971 open-source packages, identifying and trimming bloated dependencies in 39 of them. For each case, we submit a corresponding pull request, 6 of which have already been accepted and merged. PYTRIM is available as an open-source project, encouraging community contributions and further development. Video demonstration: https://youtu.be/LqTEdOUbJRI Code repository: https://github.com/TrimTeam/PyTrim

cs.SE