TY - JOUR

T1 - Malware detection in cloud computing infrastructures

AU - Watson, Michael

AU - Shirazi, Syed Noor Ul Hassan

AU - Marnerides, Angelos

AU - Mauthe, Andreas Ulrich

AU - Hutchison, David

N1 - ©2015 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2016/3

Y1 - 2016/3

N2 - Cloud services are prominent within the private, public and commercial domains. Many of these services are expected to be always on and have a critical nature; therefore, security and resilience are increasingly important aspects. In order to remain resilient, a cloud needs to possess the ability to react not only to known threats, but also to new challenges that target cloud infrastructures. In this paper we introduce and discuss an online cloud anomaly detection approach, comprising dedicated detection components of our cloud resilience architecture. More specifically, we exhibit the applicability of novelty detection under the one-class support Vector Machine (SVM) formulation at the hypervisor level, through the utilisation of features gathered at the system and network levels of a cloud node. We demonstrate that our scheme can reach a high detection accuracy of over 90% whilst detecting various types of malware and DoS attacks. Furthermore, we evaluate the merits of considering not only system-level data, but also network-level data depending on the attack type. Finally, the paper shows that our approach to detection using dedicated monitoring components per VM is particularly applicable to cloud scenarios and leads to a flexible detection system capable of detecting new malware strains with no prior knowledge of their functionality or their underlying instructions.

AB - Cloud services are prominent within the private, public and commercial domains. Many of these services are expected to be always on and have a critical nature; therefore, security and resilience are increasingly important aspects. In order to remain resilient, a cloud needs to possess the ability to react not only to known threats, but also to new challenges that target cloud infrastructures. In this paper we introduce and discuss an online cloud anomaly detection approach, comprising dedicated detection components of our cloud resilience architecture. More specifically, we exhibit the applicability of novelty detection under the one-class support Vector Machine (SVM) formulation at the hypervisor level, through the utilisation of features gathered at the system and network levels of a cloud node. We demonstrate that our scheme can reach a high detection accuracy of over 90% whilst detecting various types of malware and DoS attacks. Furthermore, we evaluate the merits of considering not only system-level data, but also network-level data depending on the attack type. Finally, the paper shows that our approach to detection using dedicated monitoring components per VM is particularly applicable to cloud scenarios and leads to a flexible detection system capable of detecting new malware strains with no prior knowledge of their functionality or their underlying instructions.

U2 - 10.1109/TDSC.2015.2457918

DO - 10.1109/TDSC.2015.2457918

M3 - Journal article

VL - 13

SP - 192

EP - 205

JO - IEEE Transactions on Dependable and Secure Computing

JF - IEEE Transactions on Dependable and Secure Computing

SN - 1545-5971

IS - 2

ER -