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583 results on '"Mercaldo, Francesco"'

2. Dynamic Analysis for Explainable Fine-Grained Android Malware Detection

Author

Ciaramella, Giovanni, Mercaldo, Francesco, Santone, Antonella, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Martinelli, Fabio, editor, and Rios, Ruben, editor

Published
2025
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6. TFE-GNN: A Temporal Fusion Encoder Using Graph Neural Networks for Fine-grained Encrypted Traffic Classification

Author

Zhang, Haozhen, Yu, Le, Xiao, Xi, Li, Qing, Mercaldo, Francesco, Luo, Xiapu, and Liu, Qixu

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Encrypted traffic classification is receiving widespread attention from researchers and industrial companies. However, the existing methods only extract flow-level features, failing to handle short flows because of unreliable statistical properties, or treat the header and payload equally, failing to mine the potential correlation between bytes. Therefore, in this paper, we propose a byte-level traffic graph construction approach based on point-wise mutual information (PMI), and a model named Temporal Fusion Encoder using Graph Neural Networks (TFE-GNN) for feature extraction. In particular, we design a dual embedding layer, a GNN-based traffic graph encoder as well as a cross-gated feature fusion mechanism, which can first embed the header and payload bytes separately and then fuses them together to obtain a stronger feature representation. The experimental results on two real datasets demonstrate that TFE-GNN outperforms multiple state-of-the-art methods in fine-grained encrypted traffic classification tasks., Comment: Accepted by The Web Conference 2023 (WWW'23). The code will be available with our incoming future work

Published
2023

7. A Robust and Explainable Deep Learning Method for Cervical Cancer Screening

Author

Di Giammarco, Marcello, Mercaldo, Francesco, Zhou, Xiaoli, Huang, Pan, Santone, Antonella, Cesarelli, Mario, Martinelli, Fabio, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Mahmud, Mufti, editor, Ben-Abdallah, Hanene, editor, Kaiser, M. Shamim, editor, Ahmed, Muhammad Raisuddin, editor, and Zhong, Ning, editor

Published
2024
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11. Towards Resilient Artificial Intelligence: Survey and Research Issues

Author

Eigner, Oliver, Eresheim, Sebastian, Kieseberg, Peter, Klausner, Lukas Daniel, Pirker, Martin, Priebe, Torsten, Tjoa, Simon, Marulli, Fiammetta, and Mercaldo, Francesco

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Cryptography and Security, Computer Science - Computers and Society
Abstract

Artificial intelligence (AI) systems are becoming critical components of today's IT landscapes. Their resilience against attacks and other environmental influences needs to be ensured just like for other IT assets. Considering the particular nature of AI, and machine learning (ML) in particular, this paper provides an overview of the emerging field of resilient AI and presents research issues the authors identify as potential future work., Comment: 7 pages, 1 figure

Published
2021
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17. Explainability of Model Checking for Mobile Malicious Behavior Between Collaborative Apps Detection and Localisation

Author

Mercaldo, Francesco, Casolare, Rosangela, Santone, Antonella, Masys, Anthony J., Editor-in-Chief, Bichler, Gisela, Advisory Editor, Bourlai, Thirimachos, Advisory Editor, Johnson, Chris, Advisory Editor, Karampelas, Panagiotis, Advisory Editor, Leuprecht, Christian, Advisory Editor, Morse, Edward C., Advisory Editor, Skillicorn, David, Advisory Editor, Yamagata, Yoshiki, Advisory Editor, Dimitrakos, Theo, editor, Lopez, Javier, editor, and Martinelli, Fabio, editor

Published
2023
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24. Explainable Deep Learning for Alzheimer Disease Classification and Localisation

Author

Di Giammarco, Marcello, Iadarola, Giacomo, Martinelli, Fabio, Mercaldo, Francesco, Ravelli, Fabrizio, Santone, Antonella, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Mahmud, Mufti, editor, Ieracitano, Cosimo, editor, Kaiser, M. Shamim, editor, Mammone, Nadia, editor, and Morabito, Francesco Carlo, editor

Published
2022
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30. On the Effectiveness of System API-Related Information for Android Ransomware Detection

Author

Scalas, Michele, Maiorca, Davide, Mercaldo, Francesco, Visaggio, Corrado Aaron, Martinelli, Fabio, and Giacinto, Giorgio

Subjects
Computer Science - Cryptography and Security, Computer Science - Machine Learning
Abstract

Ransomware constitutes a significant threat to the Android operating system. It can either lock or encrypt the target devices, and victims are forced to pay ransoms to restore their data. Hence, the prompt detection of such attacks has a priority in comparison to other malicious threats. Previous works on Android malware detection mainly focused on Machine Learning-oriented approaches that were tailored to identifying malware families, without a clear focus on ransomware. More specifically, such approaches resorted to complex information types such as permissions, user-implemented API calls, and native calls. However, this led to significant drawbacks concerning complexity, resilience against obfuscation, and explainability. To overcome these issues, in this paper, we propose and discuss learning-based detection strategies that rely on System API information. These techniques leverage the fact that ransomware attacks heavily resort to System API to perform their actions, and allow distinguishing between generic malware, ransomware and goodware. We tested three different ways of employing System API information, i.e., through packages, classes, and methods, and we compared their performances to other, more complex state-of-the-art approaches. The attained results showed that systems based on System API could detect ransomware and generic malware with very good accuracy, comparable to systems that employed more complex information. Moreover, the proposed systems could accurately detect novel samples in the wild and showed resilience against static obfuscation attempts. Finally, to guarantee early on-device detection, we developed and released on the Android platform a complete ransomware and malware detector (R-PackDroid) that employed one of the methodologies proposed in this paper.

Published
2018
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33. Explainable Security Requirements Classification Through Transformer Models.

Author

Petrillo, Luca, Martinelli, Fabio, Santone, Antonella, and Mercaldo, Francesco

Subjects
TRANSFORMER models, SECURITY classification (Government documents), COMPUTER software development, COMPUTER software security, COMPUTER software
Abstract

Security and non-security requirements are two critical issues in software development. Classifying requirements is crucial as it aids in recalling security needs during the early stages of development, ultimately leading to enhanced security in the final software solution. However, it remains a challenging task to classify requirements into security and non-security categories automatically. In this work, we propose a novel method for automatically classifying software requirements using transformer models to address these challenges. In this work, we fine-tuned four pre-trained transformers using four datasets (the original one and the three augmented versions). In addition, we employ few-shot learning techniques by leveraging transfer learning models, explicitly utilizing pre-trained architectures. The study demonstrates that these models can effectively classify security requirements with reasonable accuracy, precision, recall, and F1-score, demonstrating that the fine-tuning and SetFit can help smaller models generalize, making them suitable for enhancing security processes in the Software Development Cycle. Finally, we introduced the explainability of fine-tuned models to elucidate how each model extracts and interprets critical information from input sequences through attention visualization heatmaps. [ABSTRACT FROM AUTHOR]

Published
2025
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40. A Neural-Network-Based Framework for Supporting Driver Classification and Analysis

Author

Cuzzocrea, Alfredo, Mercaldo, Francesco, Martinelli, Fabio, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Gervasi, Osvaldo, editor, Murgante, Beniamino, editor, Misra, Sanjay, editor, Garau, Chiara, editor, Blečić, Ivan, editor, Taniar, David, editor, Apduhan, Bernady O., editor, Rocha, Ana Maria A. C., editor, Tarantino, Eufemia, editor, and Torre, Carmelo Maria, editor

Published
2021
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41. A Framework for Supporting Ransomware Detection and Prevention Based on Hybrid Analysis

Author

Cuzzocrea, Alfredo, Mercaldo, Francesco, Martinelli, Fabio, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Gervasi, Osvaldo, editor, Murgante, Beniamino, editor, Misra, Sanjay, editor, Garau, Chiara, editor, Blečić, Ivan, editor, Taniar, David, editor, Apduhan, Bernady O., editor, Rocha, Ana Maria A. C., editor, Tarantino, Eufemia, editor, and Torre, Carmelo Maria, editor

Published
2021
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42. A Machine-Learning-Based Framework for Supporting Malware Detection and Analysis

Author

Cuzzocrea, Alfredo, Mercaldo, Francesco, Martinelli, Fabio, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Gervasi, Osvaldo, editor, Murgante, Beniamino, editor, Misra, Sanjay, editor, Garau, Chiara, editor, Blečić, Ivan, editor, Taniar, David, editor, Apduhan, Bernady O., editor, Rocha, Ana Maria A. C., editor, Tarantino, Eufemia, editor, and Torre, Carmelo Maria, editor

Published
2021
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