In this tutorial, we will apply Graph Convolutional Network (GCN) and Graph Attention Network (GAT) to detect fraudulent bitcoin transactions. Also, we will compare their performances.

In this article, we will consider approaches to detect obfuscated JavaScript code snippets using machine learning.

According to Open Web Application Security Project (OWASP) ratings of top cybersecurity risks, among which web application risks, mobile and IoT devices risks, machine learning systems vulnerabilities, the problem of protection in the digital space continues to be extremely relevant. Here we will consider actual challenges which are arising from the conjunction of machine learning and cybersecurity applications in different areas such as IoT ecosystems, targeted advanced persistent threats, fraud and anomalies detection, etc. Also, you will find interesting ideas for your future machine learning projects.

Phishing is a fraudulent activity that aims to steal user credentials, credit card and bank account information or to deploy malicious software on the victim’s infrastructure. For instance, fraudster can massively send letters containing malicious links, by clicking on which the user allows the attacker to get sensitive data. There are various phishing URL detection techniques: white/black lists usage, heuristics oriented approaches such as usage of content, visual and URL features. Here we will discuss URL-based method using standard machine learning approaches (Logistic regression, Random forest, Stochastic gradient descent classifier and etc.) in combination with NLP driven features which can be extracted from the URL.

In this tutorial we will consider colorectal histology tissues classification using ResNet architecture and Pytorch framework.