Deep learning model for preicting multistage cyber attacks

Abstract

The prediction of cyberattacks has been a major concern in cybersecurity. This is due to the huge financial and resource losses incurred by organisations after a cyberattack. The emergence of new applications and disruptive technologies has come with new vulnerabilities, most of which are novel – with no immediate remediation available. Recent attacks signatures are becoming evasive, deploying very complex techniques and algorithms to infiltrate a network, leading to unauthorized access and modification of system parameters and classified data. Although there exists several approaches to mitigating attacks, challenges of using known attack signatures and modeled behavioural profiles of network environments still linger. Consequently, this paper discusses the use of unsupervised statistical and supervised deep learning techniques to predict attacks by mapping hyper-alerts to class labels of attacks. This enhances the processes of feature extraction and transformation, as a means of giving structured interpretation of the dynamic profiles of a network.

Keywords: Alert correlation, Cyberattack prediction, Cybersecurity, Deep learning, Cyberattacks, Supervised and Unsupervised Learning

Vol. 26 No 1, June 2019