Search Results for author: Luigi V. Mancini

Found 11 papers, 1 papers with code

FedComm: Federated Learning as a Medium for Covert Communication

no code implementations21 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.

Federated Learning

MalPhase: Fine-Grained Malware Detection Using Network Flow Data

no code implementations1 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.

Denoising Malware Detection

PassFlow: Guessing Passwords with Generative Flows

no code implementations13 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.

Image Generation

Reliable Detection of Compressed and Encrypted Data

no code implementations31 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.

Capture the Bot: Using Adversarial Examples to Improve CAPTCHA Robustness to Bot Attacks

no code implementations30 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.

EnCoD: Distinguishing Compressed and Encrypted File Fragments

no code implementations15 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.

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