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SANE, Bernard Ousmane, BA, Mandicou, FALL, Doudou, KASHIHARA, Shigeru, TAENAKA, Yuzo, NIANG, Ibrahima, Kadobayashi, Youki.  2020.  Solving the Interdependency Problem: A Secure Virtual Machine Allocation Method Relying on the Attacker’s Efficiency and Coverage. 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID). :440—449.
Cloud computing dominates the information communication and technology landscape despite the presence of lingering security issues such as the interdependency problem. The latter is a co-residence conundrum where the attacker successfully compromises his target virtual machine by first exploiting the weakest (in terms of security) virtual machine that is hosted in the same server. To tackle this issue, we propose a novel virtual machine allocation policy that is based on the attacker's efficiency and coverage. By default, our allocation policy considers all legitimate users as attackers and then proceeds to host the users' virtual machines to the server where their efficiency and/or coverage are the smallest. Our simulation results show that our proposal performs better than the existing allocation policies that were proposed to tackle the same issue, by reducing the attacker's possibilities to zero and by using between 30 - 48% less hosts.
Prabhakar, Kashish, Dutta, Kaushik, Jain, Rachana, Sharma, Mayank, Khatri, Sunil Kumar.  2019.  Securing Virtual Machines on Cloud through Game Theory Approach. 2019 Amity International Conference on Artificial Intelligence (AICAI). :859–863.

With the ever so growing boundaries for security in the cloud, it is necessary to develop ways to prevent from total cloud server failure. In this paper, we try to design a Game Strategy Block that sets up rules for security based on a tower defence game to secure the hypervisor from potential threats. We also try to define a utility function named the Virtual Machine Vitality Measure (VMVM) that could enlighten on the status of the virtual machines on the virtual environment.

Misono, Masanori, Yoshida, Kaito, Hwang, Juho, Shinagawa, Takahiro.  2018.  Distributed Denial of Service Attack Prevention at Source Machines. 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech). :488-495.

Distributed denial of service (DDoS) attacks is a serious cyberattack that exhausts target machine's processing capacity by sending a huge number of packets from hijacked machines. To minimize resource consumption caused by DDoS attacks, filtering attack packets at source machines is the best approach. Although many studies have explored the detection of DDoS attacks, few studies have proposed DDoS attack prevention schemes that work at source machines. We propose a reliable, lightweight, transparent, and flexible DDoS attack prevention scheme that works at source machines. In this scheme, we employ a hypervisor with a packet filtering mechanism on each managed machine to allow the administrator to easily and reliably suppress packet transmissions. To make the proposed scheme lightweight and transparent, we exploit a thin hypervisor that allows pass-through access to hardware (except for network devices) from the operating system, thereby reducing virtualization overhead and avoiding compromising user experience. To make the proposed scheme flexible, we exploit a configurable packet filtering mechanism with a guaranteed safe code execution mechanism that allows the administrator to provide a filtering policy as executable code. In this study, we implemented the proposed scheme using BitVisor and the Berkeley Packet Filter. Experimental results show that the proposed scheme can suppress arbitrary packet transmissions with negligible latency and throughput overhead compared to a bare metal system without filtering mechanisms.

Ishiguro, Kenta, Kono, Kenji.  2018.  Hardening Hypervisors Against Vulnerabilities in Instruction Emulators. Proceedings of the 11th European Workshop on Systems Security. :7:1–7:6.

Vulnerabilities in hypervisors are crucial in multi-tenant clouds and attractive for attackers because a vulnerability in the hypervisor can undermine all the virtual machine (VM) security. This paper focuses on vulnerabilities in instruction emulators inside hypervisors. Vulnerabilities in instruction emulators are not rare; CVE-2017-2583, CVE-2016-9756, CVE-2015-0239, CVE-2014-3647, to name a few. For backward compatibility with legacy x86 CPUs, conventional hypervisors emulate arbitrary instructions at any time if requested. This design leads to a large attack surface, making it hard to get rid of vulnerabilities in the emulator. This paper proposes FWinst that narrows the attack surface against vulnerabilities in the emulator. The key insight behind FWinst is that the emulator should emulate only a small subset of instructions, depending on the underlying CPU micro-architecture and the hypervisor configuration. FWinst recognizes emulation contexts in which the instruction emulator is invoked, and identifies a legitimate subset of instructions that are allowed to be emulated in the current context. By filtering out illegitimate instructions, FWinst narrows the attack surface. In particular, FWinst is effective on recent x86 micro-architectures because the legitimate subset becomes very small. Our experimental results demonstrate FWinst prevents existing vulnerabilities in the emulator from being exploited on Westmere micro-architecture, and the runtime overhead is negligible.

Sprabery, R., Estrada, Z. J., Kalbarczyk, Z., Iyer, R., Bobba, R. B., Campbell, R..  2017.  Trustworthy Services Built on Event-Based Probing for Layered Defense. 2017 IEEE International Conference on Cloud Engineering (IC2E). :215–225.

Numerous event-based probing methods exist for cloud computing environments allowing a hypervisor to gain insight into guest activities. Such event-based probing has been shown to be useful for detecting attacks, system hangs through watchdogs, and for inserting exploit detectors before a system can be patched, among others. Here, we illustrate how to use such probing for trustworthy logging and highlight some of the challenges that existing event-based probing mechanisms do not address. Challenges include ensuring a probe inserted at given address is trustworthy despite the lack of attestation available for probes that have been inserted dynamically. We show how probes can be inserted to ensure proper logging of every invocation of a probed instruction. When combined with attested boot of the hypervisor and guest machines, we can ensure the output stream of monitored events is trustworthy. Using these techniques we build a trustworthy log of certain guest-system-call events. The log powers a cloud-tuned Intrusion Detection System (IDS). New event types are identified that must be added to existing probing systems to ensure attempts to circumvent probes within the guest appear in the log. We highlight the overhead penalties paid by guests to increase guarantees of log completeness when faced with attacks on the guest kernel. Promising results (less that 10% for guests) are shown when a guest relaxes the trade-off between log completeness and overhead. Our demonstrative IDS detects common attack scenarios with simple policies built using our guest behavior recording system.

Vavala, B., Neves, N., Steenkiste, P..  2017.  Secure Tera-scale Data Crunching with a Small TCB. 2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). :169–180.

Outsourcing services to third-party providers comes with a high security cost-to fully trust the providers. Using trusted hardware can help, but current trusted execution environments do not adequately support services that process very large scale datasets. We present LASTGT, a system that bridges this gap by supporting the execution of self-contained services over a large state, with a small and generic trusted computing base (TCB). LASTGT uses widely deployed trusted hardware to guarantee integrity and verifiability of the execution on a remote platform, and it securely supplies data to the service through simple techniques based on virtual memory. As a result, LASTGT is general and applicable to many scenarios such as computational genomics and databases, as we show in our experimental evaluation based on an implementation of LAST-GT on a secure hypervisor. We also describe a possible implementation on Intel SGX.

Johnston, Reece, Kim, Sun-il, Coe, David, Etzkorn, Letha, Kulick, Jeffrey, Milenkovic, Aleksandar.  2016.  Xen Network Flow Analysis for Intrusion Detection. Proceedings of the 11th Annual Cyber and Information Security Research Conference. :18:1–18:4.

Virtualization technology has become ubiquitous in the computing world. With it, a number of security concerns have been amplified as users run adjacently on a single host. In order to prevent attacks from both internal and external sources, the networking of such systems must be secured. Network intrusion detection systems (NIDSs) are an important tool for aiding this effort. These systems work by analyzing flow or packet information to determine malicious intent. However, it is difficult to implement a NIDS on a virtualized system due to their complexity. This is especially true for the Xen hypervisor: Xen has incredible heterogeneity when it comes to implementation, making a generic solution difficult. In this paper, we analyze the network data flow of a typical Xen implementation along with identifying features common to any implementation. We then explore the benefits of placing security checks along the data flow and promote a solution within the hypervisor itself.

A. K. M. A., J. C. D..  2015.  "Execution Time Measurement of Virtual Machine Volatile Artifacts Analyzers". 2015 IEEE 21st International Conference on Parallel and Distributed Systems (ICPADS). :314-319.

Due to a rapid revaluation in a virtualization environment, Virtual Machines (VMs) are target point for an attacker to gain privileged access of the virtual infrastructure. The Advanced Persistent Threats (APTs) such as malware, rootkit, spyware, etc. are more potent to bypass the existing defense mechanisms designed for VM. To address this issue, Virtual Machine Introspection (VMI) emerged as a promising approach that monitors run state of the VM externally from hypervisor. However, limitation of VMI lies with semantic gap. An open source tool called LibVMI address the semantic gap. Memory Forensic Analysis (MFA) tool such as Volatility can also be used to address the semantic gap. But, it needs to capture a memory dump (RAM) as input. Memory dump acquires time and its analysis time is highly crucial if Intrusion Detection System IDS (IDS) depends on the data supplied by FAM or VMI tool. In this work, live virtual machine RAM dump acquire time of LibVMI is measured. In addition, captured memory dump analysis time consumed by Volatility is measured and compared with other memory analyzer such as Rekall. It is observed through experimental results that, Rekall takes more execution time as compared to Volatility for most of the plugins. Further, Volatility and Rekall are compared with LibVMI. It is noticed that examining the volatile data through LibVMI is faster as it eliminates memory dump acquire time.

Nikolai, J., Yong Wang.  2014.  Hypervisor-based cloud intrusion detection system. Computing, Networking and Communications (ICNC), 2014 International Conference on. :989-993.

Shared resources are an essential part of cloud computing. Virtualization and multi-tenancy provide a number of advantages for increasing resource utilization and for providing on demand elasticity. However, these cloud features also raise many security concerns related to cloud computing resources. In this paper, we propose an architecture and approach for leveraging the virtualization technology at the core of cloud computing to perform intrusion detection security using hypervisor performance metrics. Through the use of virtual machine performance metrics gathered from hypervisors, such as packets transmitted/received, block device read/write requests, and CPU utilization, we demonstrate and verify that suspicious activities can be profiled without detailed knowledge of the operating system running within the virtual machines. The proposed hypervisor-based cloud intrusion detection system does not require additional software installed in virtual machines and has many advantages compared to host-based and network based intrusion detection systems which can complement these traditional approaches to intrusion detection.