Research Article
Federated Learning for the Efficient Detection of Steganographic Threats Hidden in Image Icons
@INPROCEEDINGS{10.1007/978-3-031-31469-8_6, author={Nunziato Cassavia and Luca Caviglione and Massimo Guarascio and Angelica Liguori and Giuseppe Surace and Marco Zuppelli}, title={Federated Learning for the Efficient Detection of Steganographic Threats Hidden in Image Icons}, proceedings={Pervasive Knowledge and Collective Intelligence on Web and Social Media. First EAI International Conference, PerSOM 2022, Messina, Italy, November 17-18, 2022, Proceedings}, proceedings_a={PERSOM}, year={2023}, month={4}, keywords={Federated Learning Information Hiding Deep Learning Malware Detection}, doi={10.1007/978-3-031-31469-8_6} }- Nunziato Cassavia
Luca Caviglione
Massimo Guarascio
Angelica Liguori
Giuseppe Surace
Marco Zuppelli
Year: 2023
Federated Learning for the Efficient Detection of Steganographic Threats Hidden in Image Icons
PERSOM
Springer
DOI: 10.1007/978-3-031-31469-8_6
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.
