Abstract
An increasing number of threat actors takes advantage of information hiding techniques to prevent detection or to drop payloads containing attack routines. With the ubiquitous diffusion of mobile applications, high-resolution icons should be considered a very attractive carrier for cloaking malicious information via steganographic mechanisms. Despite machine learning approaches proven to be effective to detect hidden payloads, the mobile scenario could challenge their deployment in realistic use cases, for instance due to scalability constraints. Therefore, this paper introduces an approach based on federated learning able to prevent hazards characterizing production-quality scenarios, including different privacy regulations and lack of comprehensive datasets. Numerical results indicate that our approach achieves performances similar to those of centralized solutions.
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Notes
- 1.
Cloud-based protection mechanisms at the basis of the Google Play Protect framework: https://developers.google.com/android/play-protect/cloud-based-protections.
- 2.
- 3.
TP is the number of positive cases correctly classified, FP is the number of negative cases incorrectly classified, FN is the number of positive cases incorrectly classified, and TN is the number of negative cases correctly classified.
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Cassavia, N., Caviglione, L., Guarascio, M., Liguori, A., Surace, G., Zuppelli, M. (2023). Federated Learning for the Efficient Detection of Steganographic Threats Hidden in Image Icons. In: Comito, C., Talia, D. (eds) Pervasive Knowledge and Collective Intelligence on Web and Social Media. PerSOM 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 494. Springer, Cham. https://doi.org/10.1007/978-3-031-31469-8_6
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