Jasmine: A new Active Learning approach to combat cybercrime

One of the reasons that the deployment of network intrusion detection methods falls short is the lack of realistic labeled datasets, which makes it challenging to develop and compare techniques. It is caused by the large amounts of effort that it takes for a cyber expert to classify network connections. This has raised the need for methods that learn from both labeled and unlabeled data which observations are best to present to the human expert. Hence, Active Learning (AL) methods are of interest.