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Your search keyword '"Conti, Mauro"' showing total 297 results
297 results on '"Conti, Mauro"'

4. Alpha Phi-Shing Fraternity: Phishing Assessment in a Higher Education Institution

Author

Casagrande, Marco, Conti, Mauro, Fedeli, Monica, and Losiouk, Eleonora

Abstract

Phishing is a common social engineering attack aimed to steal personal information. Universities attract phishing attacks because: (1) they store employees and students sensitive data; (2) they save confidential documents; and (3) their infrastructures often lack security. In this paper, we showcase a phishing assessment at the University of Redacted aimed to identify the people, and the features of such people, that are more susceptible to phishing attacks. We delivered phishing emails to 1.508 subjects in three separate batches, collecting a clickrate equal to 30%, 11% and 13%, respectively. We considered several features (i.e., age, gender, role, working/studying field, email template) in univariate and multivariate analyses and found that students are more susceptible to phishing attacks than professors or technical/administrative staff, and that emails designed through a spearphishing approach receive a highest clickrate. We believe this work provides the foundations for setting up an effective educational campaign to prevent phishing attacks not only at the University of Redacted, but in any other university

Published
2023

45. Multi-SpacePhish: Extending the Evasion-space of Adversarial Attacks against Phishing Website Detectors Using Machine Learning.

Author

Yuan, Ying, Apruzzese, Giovanni, and Conti, Mauro

Subjects
PHISHING, DETECTORS, ARTIFICIAL intelligence, DEEP learning, MACHINE learning
Abstract

Existing literature on adversarial Machine Learning (ML) focuses either on showing attacks that break every ML model or defenses that withstand most attacks. Unfortunately, little consideration is given to the actual feasibility of the attack or the defense. Moreover, adversarial samples are often crafted in the "feature-space," making the corresponding evaluations of questionable value. Simply put, the current situation does not allow one to estimate the actual threat posed by adversarial attacks, leading to a lack of secure ML systems. We aim to clarify such confusion in this article. By considering the application of ML for Phishing Website Detection (PWD), we formalize the "evasion-space," in which an adversarial perturbation can be introduced to fool an ML-PWD—demonstrating that even perturbations in the "feature-space" are useful. Then, we propose a realistic threat model describing evasion attacks against ML-PWD that are cheap to stage, and hence intrinsically more attractive for real phishers. After that, we perform the first statistically validated assessment of state-of-the-art ML-PWD against 12 evasion attacks. Our evaluation shows (i) the true efficacy of evasion attempts that are more likely to occur; and (ii) the impact of perturbations crafted in different evasion-spaces; our realistic evasion attempts induce a statistically significant degradation (3–10% at p< 0.05), and their cheap cost makes them a subtle threat. Notably, however, some ML-PWD are immune to our most realistic attacks (p = 0.22). Finally, as an additional contribution of this journal publication, we are the first to propose and empirically evaluate the intriguing case wherein an attacker introduces perturbations in multiple evasion-spaces at the same time. These new results show that simultaneously applying perturbations in the problem- and feature-space can cause a drop in the detection rate from 0.95 to 0. Our contribution paves the way for a much-needed re-assessment of adversarial attacks against ML systems for cybersecurity. [ABSTRACT FROM AUTHOR]

Published
2024
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46. Introduction to the Special Issue on Integrity of Multimedia and Multimodal Data in Internet of Things.

Author

Singh, Amit Kumar, Kundur, Deepa, and Conti, Mauro

Subjects
INTERNET of things, REINFORCEMENT learning, MULTIMEDIA systems, DEEP reinforcement learning, MULTIMODAL user interfaces, DEEP learning, DRIVER assistance systems
Abstract

An introduction is presented in which the editor discusses articles in the issue on topics including the integrity of multimedia and multimodal data in the Internet of Things (IoT), privacy protection methods, and advanced data analysis techniques.

Published
2024
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