Introduction in Deep Learning for Cybersecurity
|Study location||Latvia, Riga|
|Type||Short-duration course, full-time|
|Nominal duration||2 weeks; 18 July–29 July 2022 (6 ECTS)|
|Awards||(Certificate on Completion)|
|Tuition fee||€800 per programme|
Participants must be enrolled in a degree related to computer science, IT, engineering, cybersecurity, electronics, programming, etc.
The entry qualification documents are accepted in the following languages: English.
Often you can get a suitable transcript from your school. If this is not the case, you will need official translations along with verified copies of the original.
Applications are accepted from the following territories (based on citizenship): Americas, Central Asia, Eastern Asia, Europe, Indonesia, Oceania, Vietnam, Western Asia.
A motivation letter must be added to your application.
The threat of cyber-attacks has long been known, essentially since the Internet became our daily routine. Like the internet itself and computer technologies that incorporate it, cyber threats are improving every year. Now artificial intelligence and machine learning work on both sides of the trenches – cybersecurity professionals and hackers. And at this moment deep learning comes into force and sets new rules in the game.
Deep learning-based methods outperform classical methods in all tasks that are too complex to describe using rules. Cybersecurity tasks often involve defending systems from human actors whose behaviour is also hard to describe with regulations. This opens interesting opportunities for cross-domain research. The interest in deep learning research has grown exponentially in recent years.
In this summer school, participants will experience an introduction in fundamental deep learning methods. Participants will be required to apply linear algebra, calculus, information theory, probability theory for cybersecurity related tasks. The course will use Python and PyTorch as a programming framework. Participants are required to be fluent in programming languages and mathematics. The mini course will include anomaly detection tasks in network traffic and breaking image-based captchas using deep learning methods. Participants will also have access to the High-Performance Cluster (HPC), also known as a supercomputer, to train models on research grade GPUs.
The programme includes workshops and lectures in:
-Introduction to Python, Numpy, Object-Oriented programming
-Linear algebra using Numpy
-Calculus, Linear regression using Numpy
-Classification using Numpy
-Regression and Classification using PyTorch
-Auto-encoders and anomaly detection in network traffic
-Image Classification models using PyTorch
-Image-based Captcha breaking using classification models