TKRD : trusted kernel rootkit detection for cybersecurity of VMs based on machine learning and memory forensic analysis

Wang, Xiao and Zhang, Jianbiao and Zhang, Ai and Ren, Jinchang (2019) TKRD : trusted kernel rootkit detection for cybersecurity of VMs based on machine learning and memory forensic analysis. Mathematical Biosciences and Engineering, 16 (4). pp. 2650-2667. ISSN 1551-0018 (https://doi.org/10.3934/mbe.2019132)

[thumbnail of Wang-etal-MBE-2019-trusted-kernel-rootkit-detection-for-cybersecurity-of-VMs-based-on-machine-learning]
Preview
 Text. Filename: Wang_etal_MBE_2019_trusted_kernel_rootkit_detection_for_cybersecurity_of_VMs_based_on_machine_learning.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (559kB)| Preview

Abstract

The promotion of cloud computing makes the virtual machine (VM) increasingly a target of malware attacks in cybersecurity such as those by kernel rootkits. Memory forensic, which observes the malicious tracks from the memory aspect, is a useful way for malware detection. In this paper, we propose a novel TKRD method to automatically detect kernel rootkits in VMs from private cloud, by combining VM memory forensic analysis with bio-inspired machine learning technology. Malicious features are extracted from the memory dumps of the VM through memory forensic analysis method. Based on these features, various machine learning classifiers are trained including Decision tree, Rule based classifiers, Bayesian and Support vector machines (SVM). The experiment results show that the Random Forest classifier has the best performance which can effectively detect unknown kernel rootkits with an Accuracy of 0.986 and an AUC value (the area under the receiver operating characteristic curve) of 0.998.

ORCID iDs

Wang, Xiao, Zhang, Jianbiao, Zhang, Ai and Ren, Jinchang ORCID logoORCID: https://orcid.org/0000-0001-6116-3194;
CORE (COnnecting REpositories)