myTrustedCloud: Trusted cloud infrastructure for security-critical computation and data managment

Copyright @ 2012 IEEECloud Computing provides an optimal infrastructure to utilise and share both computational and data resources whilst allowing a pay-per-use model, useful to cost-effectively manage hardware investment or to maximise its utilisation. Cloud Computing also offers transitory access to scalable amounts of computational resources, something that is particularly important due to the time and financial constraints of many user communities. The growing number of communities that are adopting large public cloud resources such as Amazon Web Services [1] or Microsoft Azure [2] proves the success and hence usefulness of the Cloud Computing paradigm. Nonetheless, the typical use cases for public clouds involve non-business critical applications, particularly where issues around security of utilization of applications or deposited data within shared public services are binding requisites. In this paper, a use case is presented illustrating how the integration of Trusted Computing technologies into an available cloud infrastructure - Eucalyptus - allows the security-critical energy industry to exploit the flexibility and potential economical benefits of the Cloud Computing paradigm for their business-critical applications

Hardening High-Assurance Security Systems with Trusted Computing

We are living in the time of the digital revolution in which the world we know changes beyond recognition every decade. The positive aspect is that these changes also drive the progress in quality and availability of digital assets crucial for our societies. To name a few examples, these are broadly available communication channels allowing quick exchange of knowledge over long distances, systems controlling automatic share and distribution of renewable energy in international power grid networks, easily accessible applications for early disease detection enabling self-examination without burdening the health service, or governmental systems assisting citizens to settle official matters without leaving their homes. Unfortunately, however, digitalization also opens opportunities for malicious actors to threaten our societies if they gain control over these assets after successfully exploiting vulnerabilities in the complex computing systems building them. Protecting these systems, which are called high-assurance security systems, is therefore of utmost importance. For decades, humanity has struggled to find methods to protect high-assurance security systems. The advancements in the computing systems security domain led to the popularization of hardware-assisted security techniques, nowadays available in commodity computers, that opened perspectives for building more sophisticated defense mechanisms at lower costs. However, none of these techniques is a silver bullet. Each one targets particular use cases, suffers from limitations, and is vulnerable to specific attacks. I argue that some of these techniques are synergistic and help overcome limitations and mitigate specific attacks when used together. My reasoning is supported by regulations that legally bind high-assurance security systems' owners to provide strong security guarantees. These requirements can be fulfilled with the help of diverse technologies that have been standardized in the last years. In this thesis, I introduce new techniques for hardening high-assurance security systems that execute in remote execution environments, such as public and hybrid clouds. I implemented these techniques as part of a framework that provides technical assurance that high-assurance security systems execute in a specific data center, on top of a trustworthy operating system, in a virtual machine controlled by a trustworthy hypervisor or in strong isolation from other software. I demonstrated the practicality of my approach by leveraging the framework to harden real-world applications, such as machine learning applications in the eHealth domain. The evaluation shows that the framework is practical. It induces low performance overhead (<6%), supports software updates, requires no changes to the legacy application's source code, and can be tailored to individual trust boundaries with the help of security policies. The framework consists of a decentralized monitoring system that offers better scalability than traditional centralized monitoring systems. Each monitored machine runs a piece of code that verifies that the machine's integrity and geolocation conform to the given security policy. This piece of code, which serves as a trusted anchor on that machine, executes inside the trusted execution environment, i.e., Intel SGX, to protect itself from the untrusted host, and uses trusted computing techniques, such as trusted platform module, secure boot, and integrity measurement architecture, to attest to the load-time and runtime integrity of the surrounding operating system running on a bare metal machine or inside a virtual machine. The trusted anchor implements my novel, formally proven protocol, enabling detection of the TPM cuckoo attack. The framework also implements a key distribution protocol that, depending on the individual security requirements, shares cryptographic keys only with high-assurance security systems executing in the predefined security settings, i.e., inside the trusted execution environments or inside the integrity-enforced operating system. Such an approach is particularly appealing in the context of machine learning systems where some algorithms, like the machine learning model training, require temporal access to large computing power. These algorithms can execute inside a dedicated, trusted data center at higher performance because they are not limited by security features required in the shared execution environment. The evaluation of the framework showed that training of a machine learning model using real-world datasets achieved 0.96x native performance execution on the GPU and a speedup of up to 1560x compared to the state-of-the-art SGX-based system. Finally, I tackled the problem of software updates, which makes the operating system's integrity monitoring unreliable due to false positives, i.e., software updates move the updated system to an unknown (untrusted) state that is reported as an integrity violation. I solved this problem by introducing a proxy to a software repository that sanitizes software packages so that they can be safely installed. The sanitization consists of predicting and certifying the future (after the specific updates are installed) operating system's state. The evaluation of this approach showed that it supports 99.76% of the packages available in Alpine Linux main and community repositories. The framework proposed in this thesis is a step forward in verifying and enforcing that high-assurance security systems execute in an environment compliant with regulations. I anticipate that the framework might be further integrated with industry-standard security information and event management tools as well as other security monitoring mechanisms to provide a comprehensive solution hardening high-assurance security systems

TCG based approach for secure management of virtualized platforms: state-of-the-art

There is a strong trend shift in the favor of adopting virtualization to get business benefits. The provisioning of virtualized enterprise resources is one kind of many possible scenarios. Where virtualization promises clear advantages it also poses new security challenges which need to be addressed to gain stakeholders confidence in the dynamics of new environment. One important facet of these challenges is establishing 'Trust' which is a basic primitive for any viable business model. The Trusted computing group (TCG) offers technologies and mechanisms required to establish this trust in the target platforms. Moreover, TCG technologies enable protecting of sensitive data in rest and transit. This report explores the applicability of relevant TCG concepts to virtualize enterprise resources securely for provisioning, establish trust in the target platforms and securely manage these virtualized Trusted Platforms

Deploying Virtual Machines on Shared Platforms

In this report, we describe mechanisms for secure deployment of virtual machines on shared platforms looking into a telecommunication cloud use case, which is also presented in this report. The architecture we present focuses on the security requirements of the major stakeholders’ part of the scenario we present. This report comprehensively covers all major security aspects including different security mechanisms and protocols, leveraging existing standards and state-of-the art wherever applicable. In particular, our architecture uses TCG technologies for trust establishment in the deployment of operator virtual machines on shared resource platforms. We also propose a novel procedure for securely launching and cryptographically binding a virtual machine to a target platform thereby protecting the operator virtual machine and its related credentials

Routing-Verification-as-a-Service (RVaaS): Trustworthy Routing Despite Insecure Providers

Computer networks today typically do not provide any mechanisms to the users to learn, in a reliable manner, which paths have (and have not) been taken by their packets. Rather, it seems inevitable that as soon as a packet leaves the network card, the user is forced to trust the network provider to forward the packets as expected or agreed upon. This can be undesirable, especially in the light of today's trend toward more programmable networks: after a successful cyber attack on the network management system or Software-Defined Network (SDN) control plane, an adversary in principle has complete control over the network. This paper presents a low-cost and efficient solution to detect misbehaviors and ensure trustworthy routing over untrusted or insecure providers, in particular providers whose management system or control plane has been compromised (e.g., using a cyber attack). We propose Routing-Verification-as-a-Service (RVaaS): RVaaS offers clients a flexible interface to query information relevant to their traffic, while respecting the autonomy of the network provider. RVaaS leverages key features of OpenFlow-based SDNs to combine (passive and active) configuration monitoring, logical data plane verification and actual in-band tests, in a novel manner

Trust based Privacy Policy Enforcement in Cloud Computing

Cloud computing offers opportunities for organizations to reduce IT costs by using the computation and storage of a remote provider. Despite the benefits offered by cloud computing paradigm, organizations are still wary of delegating their computation and storage to a cloud service provider due to trust concerns. The trust issues with the cloud can be addressed by a combination of regulatory frameworks and supporting technologies. Privacy Enhancing Technologies (PET) and remote attestation provide the technologies for addressing the trust concerns. PET provides proactive measures through cryptography and selective dissemination of data to the client. Remote attestation mechanisms provides reactive measures by enabling the client to remotely verify if a provider is compromised. The contributions of this work are three fold. This thesis explores the PET landscape by studying in detail the implications of using PET in cloud architectures. The practicality of remote attestation in Software as a Service (SaaS) and Infrastructure as a Service (IaaS) scenarios is also analyzed and improvements have been proposed to the state of the art. This thesis also propose a fresh look at trust relationships in cloud computing, where a single provider changes its configuration for each client based on the subjective and dynamic trust assessments of clients. We conclude by proposing a plan for expanding on the completed work