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.
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 • 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 • 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 • 12 Feb 2022 • Giulio Pagnotta, Dorjan Hitaj, Briland Hitaj, Fernando Perez-Cruz, Luigi V. Mancini
Being trained on proprietary information, these models provide a competitive edge for the owner company.
no code implementations • 21 Jan 2022 • Dorjan Hitaj, Giulio Pagnotta, Briland Hitaj, Fernando Perez-Cruz, Luigi V. Mancini
Proposed as a solution to mitigate the privacy implications related to the adoption of deep learning, Federated Learning (FL) enables large numbers of participants to successfully train deep neural networks without having to reveal the actual private training data.
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 • 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.
1 code implementation • 2 Mar 2021 • Dorjan Hitaj, Giulio Pagnotta, Iacopo Masi, Luigi V. Mancini
The original model shows an accuracy of 59% under AutoAttack - when trained with additional data with pseudo-labels.
no code implementations • 30 Oct 2020 • Dorjan Hitaj, Briland Hitaj, Sushil Jajodia, Luigi V. Mancini
To this date, CAPTCHAs have served as the first line of defense preventing unauthorized access by (malicious) bots to web-based services, while at the same time maintaining a trouble-free experience for human visitors.
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 • 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 • 3 Sep 2018 • Dorjan Hitaj, Luigi V. Mancini
The watermark allows the legitimate owner to detect copyright infringements of his model.