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Toutes nos formations

Machine Learning and Cybersecurity: why and how with scikit-learn

 Module état de l'art 
With fast development of AI techniques, it becomes a must to understand how AI would help predict future cyber security incidents and suggest proactive defence actions to mitigate potential cyber attacks. © Inria / Photo C. Morel

Session:

Aucune session disponible actuellement.

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Objectifs

We aim to reach two goals in this training program. First of all, we will introduce the popular AI-driven cyber security applications. We will especially focus on malware classification and malware clustering tasks that are necessary for most of commercialised Anti-Virus vendors. Second, we will propose several use cases of using AI toolboxes, such as Scikit-learn APIs, to analyse the statistics of malware samples, extract malware features and perform classification / clustering test. We will learn to how to set up a fair benchmark to evaluate the performances of AI-based security incident detection and showcase the potential impacts of dataset design over the evaluation result.

Target audience: R&D engineers and researchers.

Keywords: scikit-learn, malware classification, AI for security.

Pré-requis

  • Preliminary knowledge about scikit-learn, Numpy , Scipy packages in Python.

Programme

AI is beyond simply recognising images or videos, but also focusing on understand attacks, encoding knowledges learnt from past security incidents and recommend possible mitigation plans. We will cover an introduction to the potential use of AI for improving cyber safety service at first and then demonstrate the basic practices of AI algorithms to reach a data-driven security incident classification.

More specifically, we will include:

  • An introduction to the modern AI technologies and landed use cases of AI in the world of cyber security vendors,
  • A use case explanation about how to set up an AI pipeline for produce the summary of malware statistics, extraction of malware features and performance evaluation of malware classification result.
  • We explain the factors that may bring impacts over the malware classification results.

Intervenant(s)

  • Yufei Han

    Chargé de recherche Inria

    Dr Yufei Han is a researcher at Inria CIDRE project-team. He has been devoted himself into the research of adversarial AI techniques and AI-boosted cybersecurity applications for over 8 years. Before he joined Inria, he used to be senior principal researcher at Symantec Research Labs and post-doctoral researcher at Inria. He has published over 30 research papers on top-tiered research venues of AI and cyber security. He has also obtained 15 US patents on AI-based malware classification and intrusion detection systems.

Les prochaines sessions

2 jours

For more information

About prices

  • Partnership with the Systematic cluster and the Pôle Aktantis: cluster member companies benefit from special rates,
  • Advanced module (2 days): €1,300 per person (€1,040 for Systematic and Aktantis cluster member companies),
  • Reduced rates for groups of 5 or more (-10% for 5 to 9 participants, -20% for more than 10 participants) and for group purchases of beginner and advanced modules (-20%),
  • All sessions other than intra-company are distance learning.

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