An Insight into Deep Learning based Cryptojacking Detection Model

Abstract

To autonomously identify cyber threats is a non-trivial research topic. One area where this is most apparent is in the evolution of evasive cyber assaults, which are becoming better at masking their existence and obscuring their attack methods (for example, file-less malware). Particularly stealthy Advanced Persistent Threats may hide out in the system for a long time without being spotted. This study presents a novel method, dubbed CapJack, for identifying illicit bitcoin mining activity in a web browser by using cutting-edge CapsNet technology. Thus far, it is aware that deep learning framework CapsNet is pertained to the problem of detecting malware effectively using a heuristic based on system behaviour. Even more, in multitasking situations when several apps are all active at the same time, it is possible to identify fraudulent miners with greater efficiency.