no code implementations • 6 Nov 2019 • Fabio De Gaspari, Dorjan Hitaj, Giulio Pagnotta, Lorenzo De Carli, Luigi V. Mancini
Recent progress in machine learning has generated promising results in behavioral malware detection.
no code implementations • 15 Oct 2020 • Fabio De Gaspari, Dorjan Hitaj, Giulio Pagnotta, Lorenzo De Carli, Luigi V. Mancini
To address this issue, we design EnCoD, a learning-based classifier which can reliably distinguish compressed and encrypted data, starting with fragments as small as 512 bytes.
no code implementations • 31 Mar 2021 • Fabio De Gaspari, Dorjan Hitaj, Giulio Pagnotta, Lorenzo De Carli, Luigi V. Mancini
We evaluate EnCoD on a dataset of 16 different file types and fragment sizes ranging from 512B to 8KB.
no code implementations • 13 May 2021 • Giulio Pagnotta, Dorjan Hitaj, Fabio De Gaspari, Luigi V. Mancini
In this paper, we propose PassFlow, a flow-based generative model approach to password guessing.
no code implementations • 1 Jun 2021 • Michal Piskozub, Fabio De Gaspari, Frederick Barr-Smith, Luigi V. Mancini, Ivan Martinovic
Economic incentives encourage malware authors to constantly develop new, increasingly complex malware to steal sensitive data or blackmail individuals and companies into paying large ransoms.
no code implementations • 26 Jan 2023 • Dorjan Hitaj, Giulio Pagnotta, Fabio De Gaspari, Lorenzo De Carli, Luigi V. Mancini
Ransomware attacks have caused billions of dollars in damages in recent years, and are expected to cause billions more in the future.
no code implementations • 1 Mar 2023 • Hristofor Miho, Giulio Pagnotta, Dorjan Hitaj, Fabio De Gaspari, Luigi V. Mancini, Georgios Koubouris, Gianluca Godino, Mehmet Hakan, Concepcion Muñoz Diez
The morphological classification consists of the visual pairwise comparison of different organs of the olive tree, where the most important organ is considered to be the endocarp.
no code implementations • 6 Mar 2024 • Dorjan Hitaj, Giulio Pagnotta, Fabio De Gaspari, Sediola Ruko, Briland Hitaj, Luigi V. Mancini, Fernando Perez-Cruz
We introduce MaleficNet 2. 0, a novel technique to embed self-extracting, self-executing malware in neural networks.
no code implementations • 20 Mar 2024 • Fabio De Gaspari, Dorjan Hitaj, Luigi V. Mancini
We thoroughly evaluate our proposed approach and compare it to existing state-of-the-art defenses using multiple architectures, datasets, and poison budgets.