Search Results for author: Giovanni Apruzzese

Found 13 papers, 6 papers with code

Machine Learning in Space: Surveying the Robustness of on-board ML models to Radiation

1 code implementation4 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.

Cloud Detection

SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection

1 code implementation30 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.

Network Intrusion Detection

Mitigating Adversarial Gray-Box Attacks Against Phishing Detectors

no code implementations11 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.

feature selection

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

1 code implementation24 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.

Phishing Website Detection

Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples

no code implementations4 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.

The Role of Machine Learning in Cybersecurity

no code implementations20 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.

BIG-bench Machine Learning

SoK: The Impact of Unlabelled Data in Cyberthreat Detection

2 code implementations18 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.

Concept-based Adversarial Attacks: Tricking Humans and Classifiers Alike

no code implementations18 Mar 2022 Johannes Schneider, Giovanni Apruzzese

We propose to generate adversarial samples by modifying activations of upper layers encoding semantically meaningful concepts.

Decision Making

The Cross-evaluation of Machine Learning-based Network Intrusion Detection Systems

1 code implementation9 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.

BIG-bench Machine Learning Network Intrusion Detection

On the Evaluation of Sequential Machine Learning for Network Intrusion Detection

no code implementations15 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).

BIG-bench Machine Learning Network Intrusion Detection +1

Hardening Random Forest Cyber Detectors Against Adversarial Attacks

no code implementations9 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.

BIG-bench Machine Learning Intrusion Detection

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