Biblio

To secure a guest operating system running on a virtual machine (VM), a monitoring method using hardware breakpoints by a virtual machine monitor is required. However, debug registers are visible to guest operating systems; thus, malicious programs on a guest operating system can detect or disable the monitoring method. This paper presents a method to hide access to debug registers from programs running on a VM. Our proposed method detects programs' access to debug registers and disguises the access as having succeeded. The register's actual value is not visible or modifiable to programs, so the monitoring method is hidden. This paper presents the basic design and evaluation results of our method.

With the extensive application of cloud computing technology developing, security is of paramount importance in Cloud Computing. In the cloud computing environment, surveys have been provided on several intrusion detection techniques for detecting intrusions. We will summarize some literature surveys of various attack taxonomy, which might cause various threats in cloud environment. Such as attacks in virtual machines, attacks on virtual machine monitor, and attacks in tenant network. Besides, we review massive existing solutions proposed in the literature, such as misuse detection techniques, behavior analysis of network traffic, behavior analysis of programs, virtual machine introspection (VMI) techniques, etc. In addition, we have summarized some innovations in the field of cloud security, such as CloudVMI, data mining techniques, artificial intelligence, and block chain technology, etc. At the same time, our team designed and implemented the prototype system of CloudI (Cloud Introspection). CloudI has characteristics of high security, high performance, high expandability and multiple functions.

Denial of service (DOS) attacks are a serious threat to network security. These attacks are often sourced from virtual machines in the cloud, rather than from the attacker's own machine, to achieve anonymity and higher network bandwidth. Past research focused on analyzing traffic on the destination (victim's) side with predefined thresholds. These approaches have significant disadvantages. They are only passive defenses after the attack, they cannot use the outbound statistical features of attacks, and it is hard to trace back to the attacker with these approaches. In this paper, we propose a DOS attack detection system on the source side in the cloud, based on machine learning techniques. This system leverages statistical information from both the cloud server's hypervisor and the virtual machines, to prevent network packages from being sent out to the outside network. We evaluate nine machine learning algorithms and carefully compare their performance. Our experimental results show that more than 99.7% of four kinds of DOS attacks are successfully detected. Our approach does not degrade performance and can be easily extended to broader DOS attacks.

In recent years, there has been a huge trend towards running network intensive applications, such as Internet servers and Cloud-based service in virtual environment, where multiple virtual machines (VMs) running on the same machine share the machine's physical and network resources. In such environment, the virtual machine monitor (VMM) virtualizes the machine's resources in terms of CPU, memory, storage, network and I/O devices to allow multiple operating systems running in different VMs to operate and access the network concurrently. A key feature of virtualization is live migration (LM) that allows transfer of virtual machine from one physical server to another without interrupting the services running in virtual machine. Live migration facilitates workload balancing, fault tolerance, online system maintenance, consolidation of virtual machines etc. However, live migration is still in an early stage of implementation and its security is yet to be evaluated. The security concern of live migration is a major factor for its adoption by the IT industry. Therefore, this paper uses the X.805 security standard to investigate attacks on live virtual machine migration. The analysis highlights the main source of threats and suggests approaches to tackle them. The paper also surveys and compares different proposals in the literature to secure the live migration.