Advanced techniques for cyber threat intelligence-based APT detection and mitigation in cloud environments

Abstract

In this study, we investigate the effectiveness of machine learning models for the detection and mitigation of Advanced Persistent Threats (APTs) in cloud environments, which pose a significant risk to cybersecurity. Using a publicly available APT malware dataset, we evaluate the performance of Random Forest and Support Vector Machines (SVMs) models. Our results demonstrate that both models achieve high accuracy scores, with the Random Forest model achieving a low mean squared error. We present the results of ROC analysis and cross validation scores for the Random Forest model, which further demonstrate its potential for APT detection and mitigation. Our study highlights the significant potential of machine learning-based approaches for improving cybersecurity in cloud environments. However, further research is necessary to evaluate the performance of both models on larger datasets and in different scenarios. To enhance the accuracy and effectiveness of APT detection and mitigation, future work will focus on investigating other machine learning algorithms and techniques, such as deep learning and natural language processing. Overall, our findings provide a promising starting point for further research in this area, emphasizing the potential for machine learning-based approaches to enhance cybersecurity in the cloud. By leveraging these advanced techniques, we can mitigate the risks associated with APT attacks and better protect sensitive data and information.