Towards Deployment Shift Inhibition Through Transfer Learning in Network Intrusion Detection

Currently, machine learning sees growing adoption in numerous domains, including critical applications, like cybersecurity. However, to fully enjoy the benefits of artificial intelligence the end-user has some high barriers to entry to circumnavigate. The deployment of machine-learning-based Network Intrusion Detection Systems requires the collection of labelled data to train the intelligent components. This is an expensive and laborious process, which necessitates expert knowledge in cyberattacks and computer networks. Even when using data collected and labelled on premises, phenomena like concept drift can cause the model to underperform - a concept known as deployment shift. This paper evaluates the use of transfer learning techniques to curb the effects of deployment shift in machine-learning-based network intrusion detection.