We offer frontal and virtual courses and workshops.

All courses conclude both theoretical and practical sessions.

Our Courses:

- Deep Learning for Cybersecurity
- Big Data Science for Cybersecurity
- Machine Learning for Cybersecurity

**Deep Learning for Cybersecurity – New**

Deep Artificial Neural Networks showed state-of-the-art results in solving many complicated problems.

in this course, we will learn how Deep Learning works and how to apply it to different cybersecurity problems.

- Malware detection using Convolutional neural network (CNN).
- Cyber term extraction using Natural Language Processing (NLP).
- Malicious domain name detection using deep autoencoder (AE).
- Traffic analysis using recurrent neural network (RNN).
- Malware analysis using embeddings.
- Anomaly detection based on user activity using deep autoencoder (AE).
- Adversarial learning techniques to bypass machine learning models.
- CVE severity prediction using Word2Vec.

**Tools:** Python, Pandas, Pytorch, Keras

**Duration:** 6 lectures, 3 hours each.

## Big Data Science for Cybersecurity

Learn how to solve cybersecurity problems with huge datasets that are not solvable on a regular PC.

Learn how to use distributed computing frameworks and machine learning libraries

- Classification of malicious emails
- Clustering users based on their behavior
- Graph analysis of network traffic

**Tools:** Python, Spark

**Duration:** 3 lectures, 3 hours each.

## Machine Learning for Cybersecurity

The basic course that will teach you how to use different machine learning techniques on cybersecurity.

this course focus on core topics from data science such as data processing, learning tasks and model validation.

- Classification of spam emails using ensemble learning (random forest and Gradient boosting trees)
- Predict user risk score (Regression)
- Clustering users based on behavior (Hierarchical, K-means and DBSCAN clustering)
- Anomaly detection of traffic (LOF, One-class-SVM)

**Tools:** Python, Pandas, Sklearn, Seaborn.

**Duration:** 6 lectures, 3 hours each.