Machine Learning and Cybersecurity

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.

By the end of this training, participants will be able to:

• Understand the main applications of machine learning in cybersecurity and their practical limitations.
• Explain how machine learning techniques can support malware analysis, classification, and clustering.
• Build a basic machine learning pipeline using scikit-learn for cybersecurity use cases.
• Extract and analyze relevant features from malware datasets.
• Train and evaluate machine learning models for malware classification tasks.
• Design fair benchmarking protocols and select appropriate evaluation metrics.
• Identify the impact of dataset design and data quality on model performance and security outcomes.
• Apply good practices when developing and assessing AI-based cybersecurity solutions.