Cybersecurity - AI, Machine & Deep Learning Methods

Formation créée le 16/09/2022. Dernière mise à jour le 23/01/2024.
Version du programme : 1

Type de formation

Formation à distance

Durée de formation

35 heures (5 jours)

Cybersecurity - AI, Machine & Deep Learning Methods


Objective: This pioneering course blends the domains of cyber security and artificial intelligence. It has been designed for cyber security professionals who want to understand and implement AI models for exploring logs, security events, and other types of data. Classifying analytics is especially encouraged as well as NLP and Image Recognition techniques based on types of data sources found in the cybersecurity domain. Where coding is needed, Python and some selected libraries will be used. The expected audience is expected to be familiar with scripting coding but is not required to master any specific language.

Objectifs de la formation

  • Understand the concepts of AI, ML and DL. Their strengths and limitations
  • Generate different visualisations of your data by applying statistical models to real cybersecurity problems in meaningful ways
  • Familiarise with ML frameworks and methods
  • Identify the best suited ML models to solve complex problems
  • Work in specific cybersecurity use cases while being supervised by an AI expert

Profil des bénéficiaires

Pour qui
  • Engineers
  • Developers
Prérequis
  • Python or similar scripting language (like R, Matlab, etc)
  • Notions in AI/Machine Learning

Contenu de la formation

Introduction to AI, ML & DL
  • DL as an approach to AI
  • Neural Networks and Types
  • Data Types
  • Strengths and Limits of ML
Sample Toy Study
  • Image recognition
  • Complex regressions Use Case
AI Frameworks
  • Keras as Reference & Doc Framework
Framework Setup & Workshop
  • PyTorch installation and Setup
  • Toy-study in Pytorch
Introduction to NLP
  • NLP and Applications
  • Data Cleaning & Preprocessing
  • Tokenization
  • Stop-words, stemming & lemmatization
  • Text Data Vectorization
  • BERT, Transformers (and Adapters)
Classification & Clustering
  • Theory
  • KNN, K-Means
NLP Sample Toy Study
  • Text classification & clustering (using scikit-learn)
Interactive Discussion
  • Put the toy-study results into test
Additional Supervised & Unsupervised methods
  • Supplemental Methods to be defined
  • Model selection and evaluation
  • Visualisation
AI in Cybersecurity
  • Analytics based on data sources
  • AI Topics in Cybersecurity
NLP Applied to Cybersecurity
  • Introduction
  • Network threat analysis
  • Text Classification Methods to Detect Malware
  • Process Behaviour Analysis
  • Abnormal system behaviour detection
Use Case 1
  • Using ML to Detect Malicious URLs
  • (Note: This can be replaced by a Transformers use-case: using a pre trained BERT model, i.e. from hugging face)
Interactive Discussion
  • Performance discussion, visualisation and comparison
Statistical Methods in ML
  • Intuition vs Statistics
  • Univariate Numerical Analysis
  • Bivariate Numerical Analysis
Examples
  • Univariate (Mean, Median, Percentile, SD)
  • Bivariate (Correlation, Pearson Correlation)
More Statistics
  • Skewness & bias estimations
  • (basic) Bayes & Max entropy methods
  • Confidence Level approach
Case 1 (Continuation)
  • Statistical Methods as a tool for performance hypothesis testing
  • Improve the DL model’s performance
AI in Cybersecurity (part 2)
  • Transaction fraud detection
  • Text-based malicious intent detection
  • Machine vs human differentiation - or - Business data risk classification
Use Case 2
  • Using ML as Intrusion Detection System - or -
  • ML for Same Person Identification (prefered choice)
Revision & Closing
  • Interactive Multiple Choices Questions Revision
  • Closing Words

Équipe pédagogique

Professionnel expert technique et pédagogique.

Suivi de l'exécution et évaluation des résultats

  • Feuilles de présence.
  • Questions orales ou écrites (QCM).
  • Mises en situation.
  • Formulaires d'évaluation de la formation.
  • Certificat de réalisation de l’action de formation.

Ressources techniques et pédagogiques

  • Espace numérique de travail
  • Documents supports de formation projetés.
  • Exposés théoriques
  • Etude de cas concrets
  • Quiz en salle
  • Mise à disposition en ligne de documents supports à la suite de la formation.