Search Results for author: Pavel Laskov

Found 7 papers, 4 papers with code

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

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.

Evasion Attacks against Machine Learning at Test Time

1 code implementation21 Aug 2017 Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Srndic, Pavel Laskov, Giorgio Giacinto, Fabio Roli

In security-sensitive applications, the success of machine learning depends on a thorough vetting of their resistance to adversarial data.

BIG-bench Machine Learning Malware Detection +1

Poisoning Attacks against Support Vector Machines

1 code implementation27 Jun 2012 Battista Biggio, Blaine Nelson, Pavel Laskov

Such attacks inject specially crafted training data that increases the SVM's test error.

Efficient and Accurate Lp-Norm Multiple Kernel Learning

no code implementations NeurIPS 2009 Marius Kloft, Ulf Brefeld, Pavel Laskov, Klaus-Robert Müller, Alexander Zien, Sören Sonnenburg

Previous approaches to multiple kernel learning (MKL) promote sparse kernel combinations and hence support interpretability.

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