1 code implementation • 4 May 2024 • Kevin Lange, Federico Fontana, Francesco Rossi, Mattia Varile, Giovanni Apruzzese
We provide factual evidence that prior work did not thoroughly examine the impact of natural hazards on ML models meant for spacecraft.
1 code implementation • 30 Apr 2023 • Giovanni Apruzzese, Pavel Laskov, Johannes Schneider
Unfortunately, the value of ML for NID depends on a plethora of factors, such as hardware, that are often neglected in scientific literature.
no code implementations • 29 Dec 2022 • Giovanni Apruzzese, Hyrum S. Anderson, Savino Dambra, David Freeman, Fabio Pierazzi, Kevin A. Roundy
Recent years have seen a proliferation of research on adversarial machine learning.
no code implementations • 11 Dec 2022 • Giovanni Apruzzese, V. S. Subrahmanian
In this paper, we propose a set of Gray-Box attacks on PDs that an adversary may use which vary depending on the knowledge that he has about the PD.
1 code implementation • 24 Oct 2022 • Ying Yuan, Giovanni Apruzzese, Mauro Conti
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 a ML-PWD -- demonstrating that even perturbations in the "feature-space" are useful.
1 code implementation • 17 Oct 2022 • Pier Paolo Tricomi, Lisa Facciolo, Giovanni Apruzzese, Mauro Conti
This paper is the first to investigate such a problem.
no code implementations • 4 Jul 2022 • Giovanni Apruzzese, Rodion Vladimirov, Aliya Tastemirova, Pavel Laskov
ML, however, is known to be vulnerable to adversarial examples; moreover, as our paper will show, the 5G context is exposed to a yet another type of adversarial ML attacks that cannot be formalized with existing threat models.
no code implementations • 20 Jun 2022 • Giovanni Apruzzese, Pavel Laskov, Edgardo Montes de Oca, Wissam Mallouli, Luis Burdalo Rapa, Athanasios Vasileios Grammatopoulos, Fabio Di Franco
This paper is the first attempt to provide a holistic understanding of the role of ML in the entire cybersecurity domain -- to any potential reader with an interest in this topic.
2 code implementations • 18 May 2022 • Giovanni Apruzzese, Pavel Laskov, Aliya Tastemirova
A potential solution to this problem are semisupervised learning (SsL) methods, which combine small labelled datasets with large amounts of unlabelled data.
no code implementations • 18 Mar 2022 • Johannes Schneider, Giovanni Apruzzese
We propose to generate adversarial samples by modifying activations of upper layers encoding semantically meaningful concepts.
1 code implementation • 9 Mar 2022 • Giovanni Apruzzese, Luca Pajola, Mauro Conti
By using XeNIDS on six well-known datasets, we demonstrate the concealed potential, but also the risks, of cross-evaluations of ML-NIDS.
no code implementations • 15 Jun 2021 • Andrea Corsini, Shanchieh Jay Yang, Giovanni Apruzzese
Recent advances in deep learning renewed the research interests in machine learning for Network Intrusion Detection Systems (NIDS).
no code implementations • 9 Dec 2019 • Giovanni Apruzzese, Mauro Andreolini, Michele Colajanni, Mirco Marchetti
The experimental results on millions of labelled network flows show that the new detector has a twofold value: it outperforms state-of-the-art detectors that are subject to adversarial attacks; it exhibits robust results both in adversarial and non-adversarial scenarios.