Malware Detection in Cloud Computing Infrastructures


Cloud services are distinguished at intervals the non-public, public and industrial domains. Several of those services are expected to be continually on and have a essential nature; so, security and resilience are increasingly vital aspects. In order to remain resilient, a cloud wants to possess the flexibility to react not solely to known threats, however conjointly to new challenges that focus on cloud infrastructures. In this paper we tend to introduce and discuss an online cloud anomaly detection approach, comprising dedicated detection parts of our cloud resilience architecture. More specifically, we have a tendency to exhibit the applicability of novelty detection below the one-category support Vector Machine (SVM) formulation at the hypervisor level, through the utilisation of options gathered at the system and network levels of a cloud node. We tend to demonstrate that our scheme can reach a high detection accuracy of over % whilst detecting various types of malware and DoS attacks. Furthermore, we tend to evaluate the merits of considering not solely system-level knowledge, but conjointly network-level knowledge relying on the attack type. Finally, the paper shows that our approach to detection using dedicated monitoring components per VM is significantly applicable to cloud situations and results in a versatile detection system capable of detecting new malware strains with no previous knowledge of their functionality or their underlying directions.

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