Fingerprinting generation for Advanced Persistent Threats (APT) detection using Machine Learning techniques

With the advent of Advanced Persistent Threats (APTs), it has become more challenging to effectively detect and comprehend computer system attacks. This paper proposed an Intrusion Detection System (IDS) to effectively detect APT activities in each stage of the APT life cycle using decision trees and gradient-boosting algorithms. In addition, this model generates APT fingerprints by optimizing APT stages or attack paths that help the model with early APT detection. This model is evaluated and validated using Dataset APT (DAPT) 2020. The proposed model proved that effectively classified APT activities with more than 97.63 accuracy in most APT stages. Furthermore, this model proved effective in generating APT fingerprints.