Design security schemes against insider attacks

Abstract

Organisations often place utmost concerns to deter external cyber threats from the network perimeter. However, there has been a growing concern to detect insider threats as it can also cause adverse effects to the organisational assets such as theft of intellectual property and sabotage of network systems. Insiders are people who have certain level of privileged access to the organisation’s system which allowed them to exfiltrate confidential data for personal gain. Hence, this makes detecting insider threats challenging as their activities may mimic legitimate users’ actions. Nevertheless, an early detection of malicious activities can help organisation to mitigate potential loss of organisational asset. This project attempt to study the application of a statistical model, logistic regression to detect insider threats.