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246 results on '"Lax, Gianluca"'

51. A Light Number-Generation Scheme for Feasible and Secure Credit-Card-Payment Solutions

Author

Buccafurri, Francesco, Lax, Gianluca, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Psaila, Giuseppe, editor, and Wagner, Roland, editor

Published
2008
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56. Enabling Selective Flooding to Reduce P2P Traffic

Author

Buccafurri, Francesco, Lax, Gianluca, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Meersman, Robert, editor, and Tari, Zahir, editor

Published
2007
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57. TLS: A Tree-Based DHT Lookup Service for Highly Dynamic Networks

Author

Buccafurri, Francesco, Lax, Gianluca, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Meersman, Robert, editor, and Tari, Zahir, editor

Published
2004
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58. Reducing Data Stream Sliding Windows by Cyclic Tree-Like Histograms

Author

Buccafurri, Francesco, Lax, Gianluca, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Boulicaut, Jean-François, editor, Esposito, Floriana, editor, Giannotti, Fosca, editor, and Pedreschi, Dino, editor

Published
2004
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61. Binary-Tree Histograms with Tree Indices

Author

Buccafurri, Francesco, Furfaro, Filippo, Lax, Gianluca, Saccá, Domenico, Goos, Gerhard, editor, Hartmanis, Juris, editor, van Leeuwen, Jan, editor, Hameurlain, Abdelkader, editor, Cicchetti, Rosine, editor, and Traunmüller, Roland, editor

Published
2002
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65. Disarming visualization-based approaches in malware detection systems

Author

Saidia Fascí, Lara, Fisichella, Marco, Lax, Gianluca, Qian, Chenyi, Saidia Fascí, Lara, Fisichella, Marco, Lax, Gianluca, and Qian, Chenyi

Abstract

Visualization-based approaches have recently been used in conjunction with signature-based techniques to detect variants of malware files. Indeed, it is sufficient to modify some byte of executable files to modify the signature and, thus, to elude a signature-based detector. In this paper, we design a GAN-based architecture that allows an attacker to generate variants of a malware in which the malware patterns found by visualization-based approaches are hidden, thus producing a new version of the malware that is not detected by both signature-based and visualization-based techniques. The experiments carried out on a well-known malware dataset show a success rate of 100% in generating new variants of malware files that are not detected from the state-of-the-art visualization-based technique.

Published
2022

70. Evading model poisoning attacks in federated learning by a long-short-term-memory-based approach.

Author

Arazzi, Marco, Lax, Gianluca, and Nocera, Antonino

Subjects
*FEDERATED learning, *DATA privacy, *DEEP learning, *LIE detectors & detection, *POISONING
Abstract

Federated Learning is designed to build a global model from a set of local learning tasks carried out by several clients. Each client trains the global model on local data and sends back only the computed model updates. Although this approach preserves data privacy, several issues arise, and model poisoning is one of the most significant issues. According to this attack, a limited number of compromised clients cooperate to cause the corruption of the global model by sending back malicious model updates. A common countermeasure to model poisoning involves discarding model updates that differ from the majority more than a suitable threshold. However, several attacks still occur that elude this countermeasure, such as LIE attack, which aims to introduce an error in the model that is less than the threshold. In this paper, we propose a new approach to detect malicious updates that is based on the use of an LSTM network suitably built and trained. The experimental validation shows that our approach is able to disarm LIE and Fang attacks, which are the most effective in this context. [ABSTRACT FROM AUTHOR]

Published
2024
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72. The Next Generation of eHealth: A Multidisciplinary Survey

Author

Suraci, Chiara, primary, De Angelis, Vincenzo, additional, Lofaro, Giuseppina, additional, Giudice, Michele Lo, additional, Marrara, Giuseppe, additional, Rinaldi, Federica, additional, Russo, Antonia, additional, Bevacqua, Martina Teresa, additional, Lax, Gianluca, additional, Mammone, Nadia, additional, Laboccetta, Antonino Mazza, additional, Morabito, Francesco Carlo, additional, and Araniti, Giuseppe, additional

Published
2022
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94. Experience

Author

Fazzolari, Michela, primary, Buccafurri, Francesco, additional, Lax, Gianluca, additional, and Petrocchi, Marinella, additional

Published
2021
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