Secure Virtualization and Multicore Platforms State-of-the-Art report

SVaM

Thin Hypervisor-Based Security Architectures for Embedded Platforms

Virtualization has grown increasingly popular, thanks to its benefits of isolation, management, and utilization, supported by hardware advances. It is also receiving attention for its potential to support security, through hypervisor-based services and advanced protections supplied to guests. Today, virtualization is even making inroads in the embedded space, and embedded systems, with their security needs, have already started to benefit from virtualization’s security potential. In this thesis, we investigate the possibilities for thin hypervisor-based security on embedded platforms. In addition to significant background study, we present implementation of a low-footprint, thin hypervisor capable of providing security protections to a single FreeRTOS guest kernel on ARM. Backed by performance test results, our hypervisor provides security to a formerly unsecured kernel with minimal performance overhead, and represents a first step in a greater research effort into the security advantages and possibilities of embedded thin hypervisors. Our results show that thin hypervisors are both possible and beneficial even on limited embedded systems, and sets the stage for more advanced investigations, implementations, and security applications in the future

Sisäkkäiset virtuaaliympäristöt

Virtual Machines have been a common computation platform in areas of cloud computing for some time now. VMs offer a decent amount of isolation for security and system resources, and from application perspective they behave much like native environments. Software containers are gaining popularity, as a new application delivery technology. Just like VMs, applications started inside containers are running in isolated environments but without the performance overhead caused by virtualization of system resources. This makes containers seem like a more effient option for VMs. In this thesis, different combinations of containers and VMs are benchmarked. For each benchmark, host environment is also measured, to understand the overhead caused by the underlying virtuel environment technology. Benchmarks used include storage and network access benchmarks, and also an application benchmark of compiling Linux kernel. As another part of the thesis, a CPU intensive workload is run on the virtualization host server. Then the benchmarks are repeated, in order to determine how much the given workload effects the benchmark score, and also if this effect can be observed from the virtualization guest side by measuring CPU steal time. Results show that containers are slightly slower in the application benchmark than the host. The main difference is expected to come from the way docker handles storage accesses. With default network configuration, the container is losing in terms of performance to the host. In every benchmark we did, VMs always lost to host and containers in performance.Virtuaalikoneista on tullut yleinen laskenta-alusta pilvitietokoneille. Ne eristävät virtuaaliympäristön muista palveluista samalla fyysisellä koneella ja sovellusten näkökulmasta ne toimivat lähes samalla tavalla kuin natiivit ympäristöt. Ohjelmistokontit ovat nousseet suosioon tehokkaana sovellusten toimitusteknologiana. Molemmat, sekä virtuaalikoneet, että ohjelmistokontit tarjoavat niiden sisällä suoritettaville sovelluksille eristetyn virtuaaliympäristön. Ohjelmistokontit eivät pyri virtualisoimaan kaikkia järjestelmän resursseja vaan käyttävät alla olevaa käyttöjärjestelmän ydintä hyväkseen. Tämä tekee ohjelmistokonteista houkuttelevan vaihtoehdon virtuaalikoneille. Tässä diplomityössä suoritettiin erilaisia suorituskykymittauksia ohjelmistokonttien ja virtuaalikoneiden avulla luoduissa ympäristöissä. Myös alla olevan isäntäkoneen natiivisuorituskyky mitattiin, josta saatiin hyvä arvo erilaisten virtuaaliympäristöjen vertailuun. Mittasimme pysyvän muistin, verkon ja sovelluksen suorituskyvyn. Sovelluksena toimi Linuxin kääntäminen lähdekoodista toimivaksi käyttöjärjestelmäksi. Tuloksemme osoittavat, että sovellussuorituskykytestissä kontit häviävät natiivijärjestelmän suorituskyvylle vain vähän. Eron oletetaan johtuvan tavasta, jolla valitsemamme konttiteknologia hoitaa pysyvän muistin lukemisen ja kirjoittamisen. Oletusverkkoasetuksilla, kontit hävisivät natiivijärjestelmälle myös. Kaikissa tekemissämme suorituskykymittauksissa virtuaalikoneet hävisivät natiivijärjestelmälle sekä ohjelmistokonteille

Designing Mixed Criticality Applications on Modern Heterogeneous MPSoC Platforms

Multiprocessor Systems-on-Chip (MPSoC) integrating hard processing cores with programmable logic (PL) are becoming increasingly common. While these platforms have been originally designed for high performance computing applications, their rich feature set can be exploited to efficiently implement mixed criticality domains serving both critical hard real-time tasks, as well as soft real-time tasks. In this paper, we take a deep look at commercially available heterogeneous MPSoCs that incorporate PL and a multicore processor. We show how one can tailor these processors to support a mixed criticality system, where cores are strictly isolated to avoid contention on shared resources such as Last-Level Cache (LLC) and main memory. In order to avoid conflicts in last-level cache, we propose the use of cache coloring, implemented in the Jailhouse hypervisor. In addition, we employ ScratchPad Memory (SPM) inside the PL to support a multi-phase execution model for real-time tasks that avoids conflicts in shared memory. We provide a full-stack, working implementation on a latest-generation MPSoC platform, and show results based on both a set of data intensive tasks, as well as a case study based on an image processing benchmark application

Tuple Space Explosion: A Denial-of-Service Attack Against a Software Packet Classifier

Efficient and highly available packet classification is fundamental for various security primitives. In this paper, we evaluate whether the de facto Tuple Space Search (TSS) packet classification algorithm used in popular software networking stacks such as the Open vSwitch is robust against low-rate denial-of-service attacks. We present the Tuple Space Explosion (TSE) attack that exploits the fundamental space/time complexity of the TSS algorithm. TSE can degrade the switch performance to 12% of its full capacity with a very low packet rate (0.7 Mbps) when the target only has simple policies such as, "allow some, but drop others". Worse, an adversary with additional partial knowledge of these policies can virtually bring down the target with the same low attack rate. Interestingly, TSE does not generate any specific traffic patterns but only requires arbitrary headers and payloads which makes it particularly hard to detect. Due to the fundamental complexity characteristics of TSS, unfortunately, there seems to be no complete mitigation to the problem. As a long-term solution, we suggest the use of other algorithms (e.g., HaRP) that are not vulnerable to the TSE attack. As a short-term countermeasure, we propose MFCGuard that carefully manages the tuple space and keeps packet classification fast

Protecting applications using trusted execution environments

While cloud computing has been broadly adopted, companies that deal with sensitive data are still reluctant to do so due to privacy concerns or legal restrictions. Vulnerabilities in complex cloud infrastructures, resource sharing among tenants, and malicious insiders pose a real threat to the confidentiality and integrity of sensitive customer data. In recent years trusted execution environments (TEEs), hardware-enforced isolated regions that can protect code and data from the rest of the system, have become available as part of commodity CPUs. However, designing applications for the execution within TEEs requires careful consideration of the elevated threats that come with running in a fully untrusted environment. Interaction with the environment should be minimised, but some cooperation with the untrusted host is required, e.g. for disk and network I/O, via a host interface. Implementing this interface while maintaining the security of sensitive application code and data is a fundamental challenge. This thesis addresses this challenge and discusses how TEEs can be leveraged to secure existing applications efficiently and effectively in untrusted environments. We explore this in the context of three systems that deal with the protection of TEE applications and their host interfaces: SGX-LKL is a library operating system that can run full unmodified applications within TEEs with a minimal general-purpose host interface. By providing broad system support inside the TEE, the reliance on the untrusted host can be reduced to a minimal set of low-level operations that cannot be performed inside the enclave. SGX-LKL provides transparent protection of the host interface and for both disk and network I/O. Glamdring is a framework for the semi-automated partitioning of TEE applications into an untrusted and a trusted compartment. Based on source-level annotations, it uses either dynamic or static code analysis to identify sensitive parts of an application. Taking into account the objectives of a small TCB size and low host interface complexity, it defines an application-specific host interface and generates partitioned application code. EnclaveDB is a secure database using Intel SGX based on a partitioned in-memory database engine. The core of EnclaveDB is its logging and recovery protocol for transaction durability. For this, it relies on the database log managed and persisted by the untrusted database server. EnclaveDB protects against advanced host interface attacks and ensures the confidentiality, integrity, and freshness of sensitive data.Open Acces

Elastic Resource Management in Distributed Clouds

The ubiquitous nature of computing devices and their increasing reliance on remote resources have driven and shaped public cloud platforms into unprecedented large-scale, distributed data centers. Concurrently, a plethora of cloud-based applications are experiencing multi-dimensional workload dynamics---workload volumes that vary along both time and space axes and with higher frequency. The interplay of diverse workload characteristics and distributed clouds raises several key challenges for efficiently and dynamically managing server resources. First, current cloud platforms impose certain restrictions that might hinder some resource management tasks. Second, an application-agnostic approach might not entail appropriate performance goals, therefore, requires numerous specific methods. Third, provisioning resources outside LAN boundary might incur huge delay which would impact the desired agility. In this dissertation, I investigate the above challenges and present the design of automated systems that manage resources for various applications in distributed clouds. The intermediate goal of these automated systems is to fully exploit potential benefits such as reduced network latency offered by increasingly distributed server resources. The ultimate goal is to improve end-to-end user response time with novel resource management approaches, within a certain cost budget. Centered around these two goals, I first investigate how to optimize the location and performance of virtual machines in distributed clouds. I use virtual desktops, mostly serving a single user, as an example use case for developing a black-box approach that ranks virtual machines based on their dynamic latency requirements. Those with high latency sensitivities have a higher priority of being placed or migrated to a cloud location closest to their users. Next, I relax the assumption of well-provisioned virtual machines and look at how to provision enough resources for applications that exhibit both temporal and spatial workload fluctuations. I propose an application-agnostic queueing model that captures the resource utilization and server response time. Building upon this model, I present a geo-elastic provisioning approach---referred as geo-elasticity---for replicable multi-tier applications that can spin up an appropriate amount of server resources in any cloud locations. Last, I explore the benefits of providing geo-elasticity for database clouds, a popular platform for hosting application backends. Performing geo-elastic provisioning for backend database servers entails several challenges that are specific to database workload, and therefore requires tailored solutions. In addition, cloud platforms offer resources at various prices for different locations. Towards this end, I propose a cost-aware geo-elasticity that combines a regression-based workload model and a queueing network capacity model for database clouds. In summary, hosting a diverse set of applications in an increasingly distributed cloud makes it interesting and necessary to develop new, efficient and dynamic resource management approaches

Side Channels in the Cloud: Isolation Challenges, Attacks, and Countermeasures

Cloud computing is based on the sharing of physical resources among several virtual machines through a virtualization layer providing software isolation. Despite advances in virtualization, data security and isolation guarantees remain important challenges for cloud providers. Some of the most prominent isolation violations come from side-channel attacks that aim at exploiting and using a leaky channel to obtain sensitive data such as encryption keys. Such channels may be created by vulnerable implementations of cryptographic algorithms, exploiting weaknesses of processor architectures or of resource sharing in the virtualization layer. In this paper, we provide a comprehensive survey of side-channel attacks (SCA) and mitigation techniques for virtualized environments, focusing on cache-based attacks. We review isolation challenges, attack classes and techniques. We also provide a layer-based taxonomy of applicable countermeasures , from the hardware to the application level, with an assessment of their effectiveness

Systemunterstützung für moderne Speichertechnologien

Trust and scalability are the two significant factors which impede the dissemination of clouds. The possibility of privileged access to customer data by a cloud provider limits the usage of clouds for processing security-sensitive data. Low latency cloud services rely on in-memory computations, and thus, are limited by several characteristics of Dynamic RAM (DRAM) such as capacity, density, energy consumption, for example. Two technological areas address these factors. Mainstream server platforms, such as Intel Software Guard eXtensions (SGX) und AMD Secure Encrypted Virtualisation (SEV) offer extensions for trusted execution in untrusted environments. Various technologies of Non-Volatile RAM (NV-RAM) have better capacity and density compared to DRAM and thus can be considered as DRAM alternatives in the future. However, these technologies and extensions require new programming approaches and system support since they add features to the system architecture: new system components (Intel SGX) and data persistence (NV-RAM). This thesis is devoted to the programming and architectural aspects of persistent and trusted systems. For trusted systems, an in-depth analysis of new architectural extensions was performed. A novel framework named EActors and a database engine named STANlite were developed to effectively use the capabilities of trusted~execution. For persistent systems, an in-depth analysis of prospective memory technologies, their features and the possible impact on system architecture was performed. A new persistence model, called the hypervisor-based model of persistence, was developed and evaluated by the NV-Hypervisor. This offers transparent persistence for legacy and proprietary software, and supports virtualisation of persistent memory.Vertrauenswürdigkeit und Skalierbarkeit sind die beiden maßgeblichen Faktoren, die die Verbreitung von Clouds behindern. Die Möglichkeit privilegierter Zugriffe auf Kundendaten durch einen Cloudanbieter schränkt die Nutzung von Clouds bei der Verarbeitung von sicherheitskritischen und vertraulichen Informationen ein. Clouddienste mit niedriger Latenz erfordern die Durchführungen von Berechnungen im Hauptspeicher und sind daher an Charakteristika von Dynamic RAM (DRAM) wie Kapazität, Dichte, Energieverbrauch und andere Aspekte gebunden. Zwei technologische Bereiche befassen sich mit diesen Faktoren: Etablierte Server Plattformen wie Intel Software Guard eXtensions (SGX) und AMD Secure Encrypted Virtualisation (SEV) stellen Erweiterungen für vertrauenswürdige Ausführung in nicht vertrauenswürdigen Umgebungen bereit. Verschiedene Technologien von nicht flüchtigem Speicher bieten bessere Kapazität und Speicherdichte verglichen mit DRAM, und können daher in Zukunft als Alternative zu DRAM herangezogen werden. Jedoch benötigen diese Technologien und Erweiterungen neuartige Ansätze und Systemunterstützung bei der Programmierung, da diese der Systemarchitektur neue Funktionalität hinzufügen: Systemkomponenten (Intel SGX) und Persistenz (nicht-flüchtiger Speicher). Diese Dissertation widmet sich der Programmierung und den Architekturaspekten von persistenten und vertrauenswürdigen Systemen. Für vertrauenswürdige Systeme wurde eine detaillierte Analyse der neuen Architekturerweiterungen durchgeführt. Außerdem wurden das neuartige EActors Framework und die STANlite Datenbank entwickelt, um die neuen Möglichkeiten von vertrauenswürdiger Ausführung effektiv zu nutzen. Darüber hinaus wurde für persistente Systeme eine detaillierte Analyse zukünftiger Speichertechnologien, deren Merkmale und mögliche Auswirkungen auf die Systemarchitektur durchgeführt. Ferner wurde das neue Hypervisor-basierte Persistenzmodell entwickelt und mittels NV-Hypervisor ausgewertet, welches transparente Persistenz für alte und proprietäre Software, sowie Virtualisierung von persistentem Speicher ermöglicht