Cyber Security Threat Intelligence Monitoring and Classification

Abstract

The remote control is widely used for its convenience and its support of resource sharing. However, it can be exploited by hackers. This work aims to prevent remote network threats using behavioral features and machine learning mechanisms. A threat intelligence monitoring engine called DEtect remote Shell Threat system (DEST) was designed and divided into three levels, depending on the hazard. The performance analysis results demonstrate that the proposed DEST system has an accuracy of 99.20% and an F1-score of 99.80%. It is superior to existing detection methods, offering 4% and 1% improvement in accuracy and F1-score.