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The Internet of Things (IoT) consists of a network of physical devices that are connected over the internet for the purpose of exchanging data with other devices and systems. IoT platforms, such as Node-RED, have been introduced in recent times to facilitate communication between different IoT devices. Hosting Node-RED on a cloud service provider might result in the confidentiality of sensitive data on Node-RED being violated by malicious attackers, since users are forced to entrust their sensitive data with the cloud service providers. Using trusted execution environments, such as AMD-SEV and Intel SGX, can mitigate several potential attacks from exposing sensitive information in Node-RED. This thesis investigates if AMD-SEV and Intel SGX are viable options to protect sensitive data in Node-RED when hosted on a cloud service provider. The work in this thesis investigates difficulties encountered when deploying Node-RED on AMD-SEV and Intel SGX, from a usability perspective. Usability is measured by running Node-RED in AMDSEV and Intel SGX, and then recording the complexity of the process. Several performance tests are conducted to measure the performance overhead of Node-RED caused by AMD-SEV. A literature review is also conducted to investigate potential vulnerabilities in AMD-SEV and Intel SGX that could undermine the security of user’s data in Node-RED. The results from this thesis finds that AMD-SEV is a viable option to protect sensitive data in Node-RED when hosted on a cloud service provider. Deploying Node-RED on AMD-SEV is found to be a relatively simple process from a usability perspective. There are some noticeable performance overhead with regards to CPU utilization and TCP throughput, but all other metrics show marginal performance overhead. The potential vulnerabilities in AMD-SEV are not found to be significant enough to make AMD-SEV unviable. The thesis finds Intel SGX to be an unviable solution primarily due to usability. The process of running Node-RED in an Intel SGX enclave is extremely complex and the results show that for most users of Node-RED, this is not viable. The security vulnerabilities found from the literature review, are not significant enough to make Intel SGX an unviable option to protect sensitive user data inNode-RED. / Internet of Things (IoT) är en nätverk av fysiska enheter som är sammankopplade via internet för att kunna skicka data till andra fysiska enheter eller system. IoTplattformar, som Node-RED, har utvecklats för att förenkla kommunikationen mellan olika IoT- enheter. Att köra Node-RED på en molntjänst kan leda till att sekretessen av känslig data på Node-RED blir kränkt av en attack mot molntjänsten. Det är på grund av att användarna av Node-RED är tvungna att tillförlita deras känsliga data till molntjänsten, som deras data kan bli kränkt. Detta problem kan förminskas genom att användarna utnyttjar trusted execution environments som AMD-SEV och Intel SGX för att skydda sin känsliga data på molntjänsten. I denna avhandling, undersöks det om AMDSEV och Intel SGX kan användas för att skydda data i Node-RED när den körs på en molntjänst. Användarvänligheten av att köra Node-RED med AMD-SEV och Intel SGX undersöks genom att uppskatta hur komplicerad denna process är. Flera tester genomförs också för att mäta vilken påverkan AMD-SEV har på prestandan av Node-RED. En litteraturöversikt genomförs också för att undersöka potentiella sårbarheter i AMD-SEV och Intel SGX som skulle kunna utnyttjas för att komma åt känslig data i Node-RED. Resultaten från avhandlingen visar att AMD-SEV kan vara användbart för att skydda känslig data i Node-RED när den körs på en molntjänst. AMDSEV är väldigt användarvänlig när Node-RED ska köras. AMD-SEV har en märkbar påverkan på prestandan av processorn och TCP- genomströmning, men för de andra faktorerna som mäts har AMD-SEV ingen större påverkan. Litteraturöversikten finner inga sårbarheter som är tillräckligt farliga för att göra AMD-SEV oanvändbar för att skydda känslig data iNode-RED. Resultaten från avhandlingen visar dock att Intel SGX inte är särskilt användbar för att skydda känslig data i Node-RED när den körs på en molntjänst. Detta är främst för att det är väldigt komplicerat att köra Node-RED i en Intel SGX enklav från en användarvänlighet synpunkt. De flesta av Node-REDs användare skulle finna det för komplicerat att använda Intel SGX för att skydda sin känsliga data. Litteraturöversikten finner inga sårbarheter allvarliga nog för att göra Intel SGX oanvändbar.

The rapid increase in the number of IoT devices and their widespread applications demands secure and scalable solutions for managing and executing IoT workflows. This thesis investigates the security of IoT workflows created in Node-RED, an open-source visual programming tool, and deployed on untrusted hosts managed by a major cloud service provider, Azure. The hypothesis was that the security of IoT workflows could be improved by utilizing a trusted execution environment, such as Intel SGX. Additionally, an assessment of consequent performance degradation was proposed. A threat model for an IoT workflow system scenario was established using the STRIDE threat modeling framework. An evaluation of the security guarantees provided by the prototype system was performed using an analysis comparing the security guarantees of underlying technologies, predominantly Intel SGX, and aggregating them to establish the security promises of the final system. The performance evaluation of the system was conducted using a set of experimental workflows, executed both natively on Linux and inside Intel SGX. The proposed prototype system was deemed to be capable of mitigating 15 out of 18 potential threats defined in the threat model, which indicates a significant threat risk reduction. However, the added security resulted in degraded performance, which was considerable when executing system calls and significantly noticeable for workflows requiring multi-threading. The results showed that node execution time inside SGX was 4.8 times slower and the mean round trip time for workflow execution was 6 times slower than the native execution. The thesis aims to provide a starting point for estimating performance degradation for potential future applications requiring secure IoT workflow deployment on untrusted hosts. / Den snabba ökningen av antalet IoT-enheter i dagens samhälle och deras breda användningsområden kräver säkra och skalbara lösningar för exekvering av IoT-arbetsflöden. Detta examensarbete undersöker säkerheten för IoT-arbetsflöden skapade i Node-RED, ett öppen källkodsverktyg för visuell programmering, i kontexten att dessa arbetsflöden exekveras på opålitliga enheter som hanteras av molntjänstföretag, som i detta fall är Azure. Hypotesen var att säkerhetsgarantin för IoT-arbetsflöden kunde förbättras genom att använda en betrodd exekveringmiljö, såsom Intel SGX. Dessutom krävdes en utvärdering av påföljderna på systemets prestanda som en konsekvens av den betrodda exekveringmiljöns användning. En hotmodell för ett IoT-arbetsflödesystem etablerades med hjälp av ramverket STRIDE. En bedömning av säkerhetsgarantierna som tillhandahålls av prototypsystemet genomfördes med hjälp av en kvalitativ analys som jämförde säkerhetsgarantier för underliggande teknologier, främst Intel SGX, och aggregerade dessa för att etablera säkerhetsgarantin för det slutgiltiga systemet. Prestandautvärderingen av systemet genomfördes med hjälp av ett antal experimentella arbetsflöden, som exekverades både direkt på Linux och inuti den betrodda exekveringsmiljön Intel SGX. I det föreslagna prototypsystemet ansågs 15 utav 18 potentiella hot som definierats i hotmodellen vara försumbara, vilket indikerar en signifikant reduktion av hotbilden. Dock resulterade den ökade säkerheten i en försämrad prestanda, som var betydande när systemanrop användes och synnerligen märkbar för flöden som krävde parallellisering. Resultaten visade att nodexekveringstiden inuti SGX var 4,8 gånger långsammare och medelvärdet för rundturstiden för exekvering av ett arbetsflöde var 6 gånger långsammare än den direkta exekveringen. Examensarbetet syftar till att ge en utgångspunkt för att bedöma prestandaförsämringen för potentiella framtida applikationer som kräver säkra IoT-arbetsflöden exekverade på opålitliga enheter.

In recent years, trusted execution environments (TEEs) have seen increasing deployment in computing devices to protect security-critical software from run-time attacks and provide isolation from an untrustworthy operating system (OS). A trusted party verifies the software that runs in a TEE using remote attestation procedures. However, the publication of transient execution attacks such as Spectre and Meltdown revealed fundamental weaknesses in many TEE architectures, including Intel Software Guard Exentsions (SGX) and Arm TrustZone. These attacks can extract cryptographic secrets, thereby compromising the integrity of the remote attestation procedure. In this work, we design and develop a TEE architecture that provides remote attestation integrity protection even when confidentiality of the TEE is compromised. We use the formally verified seL4 microkernel to build the TEE, which ensures strong isolation and integrity. We offload cryptographic operations to a secure co-processor that does not share any vulnerable microarchitectural hardware units with the main processor, to protect against transient execution attacks. Our design guarantees integrity of the remote attestation procedure. It can be extended to leverage co-processors from Google and Apple, for wide-scale deployment on mobile devices. / Under de senaste åren används betrodda exekveringsmiljöer (TEE) allt mera i datorutrustning för att skydda säkerhetskritisk programvara från attacker och för att isolera dem från ett opålitligt operativsystem. En betrodd part verifierar programvaran som körs i en TEE med hjälp av fjärrattestering. Nyliga mikroarkitekturella anfall, t.ex. Spectre och Meltdown, har dock visat grundläggande svagheter i många TEE-arkitekturer, inklusive Intel SGX och Arm TrustZone. Dessa attacker kan avslöja kryptografiska hemligheter och därmed äventyra integriteten av fjärrattestning. I det här arbetet utvecklar vi en arkitektur för en betrodd exekveringsmiljö (TEE) som ger integritetsskydd genom fjärrattestering även när TEE:s konfidentialitet äventyras. Vi använder den formellt verifierade seL4-mikrokärnan för att bygga TEE:n som garanterar stark isolering och integritet. För att skydda kryptografiska operationer, overför vi dem till en säker samprocessor som inte delar någon sårbar mikroarkitektur med huvudprocessorn. Vår arktektur garanterar fjärrattesteringens integritet och kan utnyttja medprocessorer från Google och Apple för att användas i stor skala på mobila enheter.

In modern computing environments, hardware resources are commonly shared, and parallel computation is more widely used. Users run their services in parallel on the same hardware and process information with different confidentiality levels every day. Running parallel tasks can cause privacy and security problems if proper isolation is not enforced. Computers need to rely on a trusted root to protect the data from malicious entities. Intel proposed the Software Guard eXtension (SGX) to create a trusted execution environment (TEE) within the processor. SGX allows developers to benefit from the hardware level isolation. SGX relies only on the hardware, and claims runtime protection even if the OS and other software components are malicious. However, SGX disregards any kind of side-channel attacks. Researchers have demonstrated that microarchitectural sidechannels are very effective in thwarting the hardware provided isolation. In scenarios that involve SGX as part of their defense mechanism, system adversaries become important threats, and they are capable of initiating these attacks. This work introduces a new and more powerful cache side-channel attack that provides system adversaries a high resolution channel. The developed attack is able to virtually track all memory accesses of SGX execution with temporal precision. As a proof of concept, we demonstrate our attack to recover cryptographic AES keys from the commonly used implementations including those that were believed to be resistant in previous attack scenarios. Our results show that SGX cannot protect critical data sensitive computations, and efficient AES key recovery is possible in a practical environment. In contrast to previous attacks which require hundreds of measurements, this is the first cache side-channel attack on a real system that can recover AES keys with a minimal number of measurements. We can successfully recover the AES key from T-Table based implementations in a known plaintext and ciphertext scenario with an average of 15 and 7 samples respectively.

Submitted by Emanuel Varela Cardoso (emanuel.varela@ufcg.edu.br) on 2018-09-06T19:24:24Z No. of bitstreams: 1 RODOLFO DE ANDRADE MARINHO SILVA – DISSERTAÇÃO (PPGCC) 2018.pdf: 798016 bytes, checksum: 4dfd41c1185e692e1c3b8a11f541a6a6 (MD5) / Made available in DSpace on 2018-09-06T19:24:24Z (GMT). No. of bitstreams: 1 RODOLFO DE ANDRADE MARINHO SILVA – DISSERTAÇÃO (PPGCC) 2018.pdf: 798016 bytes, checksum: 4dfd41c1185e692e1c3b8a11f541a6a6 (MD5) Previous issue date: 2018-03-01 / No decorrer das últimas décadas, uma quantidade de dados de usuários cada vez maior vem sendo enviada para ambientes não controlados pelos mesmos. Em alguns casos esses dados são enviados com o objetivo de tornar esses dados públicos, mas na grande maioria das vezes há a necessidade de manter esses dados seguros e privados, ou autorizar o seu acesso apenas em usos bem específicos. Considerando o caso onde os dados devem ser mantidos privados, entidades devem tomar cuidados especiais para manter a segurança e privacidade de tais dados tanto durante a transmissão quanto durante o armazenamento e processamento dos mesmos. Com esse objetivo, vários esforços vêm sendo feitos, inclusive o desenvolvimento de componentes de hardware que provêem ambientes de execução confiável,TEEs, como o Intel Software Guard Extensions(SGX). O uso dessa tecnologia, porém, pode ser feito de forma incorreta ou ineficiente, devido a cuidados não observados durante o desenvolvimento de aplicações. O trabalho apresentado nessa dissertação aborda os principais desafios enfrentados no desenvolvimento de aplicações que façam uso deSGX, e propõe boas práticas e um conjunto de ferramentas (DynSGX) que ajudam a fazer melhor uso das capacidades da tecnologia. Tais desafios incluem, mas não são limitados a, particionamento de aplicações de acordo com o modelo de programação do SGX, colocação de aplicações em ambientes de computação na nuvem, e, sobretudo, gerência de memória. Os estudos apresentados neste trabalho apontam que o mal uso da tecnologia pode acarretar em uma perda de performance considerável se comparado com implementações que levam em conta as boas práticas propostas. O conjunto de ferramentas proposto neste trabalho também mostrou possibilitar a proteção de código de aplicações em ambientes de computação na nuvem, com uma sobrecarga desprezível em comparação com o modelo de programação padrão de SGX. / During the last few decades, an increasing amount of user data have been sent to environments not controlled by data owners. In some cases these data are sent with the objective to turn them public, but in the vast majority of times, these data need to be kept safe and private, or to be allowed access only in very specific use cases. Considering the case where data need to be kept private, entities must take specific measures to maintain the data security and privacy while transmitting, storing and processing them. With this objective many efforts have been made, including the specification of hardware components that provide a trusted execution environment (TEEs), like the Intel Software Guard Extensions (SGX). The use of this technology , though, can be made in incorrect or ineffective ways, due to not taking some considerations into account during the development of applications. In this work, we approach the main challenges faced in the development of applications that use SGX, and propose good practices and a toolset (DynSGX) that help making better use of the capabilities of this technology. Such challenges include, but are not limited to, application partitioning, application colocation in cloud computing environments, and memory management. The studies presented in this work show that the bad use of this technology can result in a considerable performance loss when compared to implementations that take into account the good practices proposed. The toolset proposed in this work also showed to enable protecting application code in cloud computing environments, having a negligible performance overhead when compared to the regular SGX programming model.

In recent years, trusted execution environments like Intel SGX have allowed developers to protect sensitive code inside so called enclaves. These enclaves protect its code and data even in the cases of a compromised OS. However, SGX enclaves have been shown to be vulnerable to numerous side-channel attacks. Therefore, there is a need to investigate ways that such attacks against enclaves can be detected. This thesis investigates the viability of using performance counters to detect an SGX-targeting side-channel attack, specifically the recent Load Value Injection (LVI) class of attacks. A case study is thus presented where performance counters and a threshold-based detection method is used to detect variants of the LVI attack. The results show that certain attack variants could be reliably detected using this approach without false positives for a range of benign applications. The results also demonstrate reasonable levels of speed and overhead for the detection tool. Some of the practical limitations of using performance counters, particularly in an SGX-context, are also brought up and discussed.

<div> <div> <div> <p>Cloud computing offers a cost-efficient data analytics platform. This is enabled by constant innovations in tools and technologies for analyzing large volumes of data through distributed batch processing systems and real-time data through distributed stream processing systems. However, due to the sensitive nature of data, many organizations are reluctant to analyze their data in public clouds. To address this stalemate, both software-based and hardware-based solutions have been proposed yet all have substantial limitations in terms of efficiency, expressiveness, and security. In this thesis, we present solutions that enable practical and expressive confidentiality- preserving batch and stream-based analytics. We achieve this by performing computations over encrypted data using Partially Homomorphic Encryption (PHE) and Property-Preserving Encryption (PPE) in novel ways, and by utilizing remote or Trusted Execution Environment (TEE) based trusted services where needed.</p><p><br></p><p>We introduce a set of extensions and optimizations to PHE and PPE schemes and propose the novel abstraction of Secure Data Types (SDTs) which enables the application of PHE and PPE schemes in ways that improve performance and security. These abstractions are leveraged to enable a set of compilation techniques making data analytics over encrypted data more practical. When PHE alone is not expressive enough to perform analytics over encrypted data, we use a novel planner engine to decide the most efficient way of utilizing client-side completion, remote re-encryption, or trusted hardware re-encryption based on Intel Software Guard eXtensions (SGX) to overcome the limitations of PHE. We also introduce two novel symmetric PHE schemes that allow arithmetic operations over encrypted data. Being symmetric, our schemes are more efficient than the state-of-the-art asymmetric PHE schemes without compromising the level of security or the range of homomorphic operations they support. We apply the aforementioned techniques in the context of batch data analytics and demonstrate the improvements over previous systems. Finally, we present techniques designed to enable the use of PHE and PPE in resource-constrained Internet of Things (IoT) devices and demonstrate the practicality of stream processing over encrypted data.</p></div></div></div><div><div><div> </div> </div> </div>

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.

<p>In today’s world, cloud machines store an ever-increasing amount of sensitive user data, but it remains challenging to guarantee the security of our data. This is because a cloud machine’s system software—critical components like the operating system and hypervisor that can access and thus leak user data—is subject to attacks by numerous other tenants and cloud administrators. Trusted execution environments (TEEs) like Intel SGX promise to alter this landscape by leveraging a trusted CPU to create execution contexts (or enclaves) where data cannot be directly accessed by system software. Unfortunately, the protection provided by TEEs cannot guarantee complete data security. In particular, our data remains unprotected if a third-party service (e.g., Yelp) running inside an enclave is adversarial. Moreover, data can be indirectly leaked from the enclave using traditional memory side-channels.</p> <p><br></p> <p>This dissertation takes a significant stride towards strong user data protection in cloud machines using TEEs by defeating the critical threats of adversarial cloud services and memory side-channels. To defeat these threats, we systematically explore both software and hardware designs. In general, we designed software solutions to avoid costly hardware changes and present faster hardware alternatives.</p> <p><br></p> <p>We designed 4 solutions for this dissertation. Our Chancel system prevents data leaks from adversarial services by restricting data access capabilities through robust and efficient compiler-enforced software sandboxing. Moreover, our Obliviate and Obfuscuro systems leverage strong cryptographic randomization and prevent information leakage through memory side-channels. We also propose minimal CPU extensions to Intel SGX called Reparo that directly close the threat of memory side-channels efficiently. Importantly, each designed solution provides principled protection by addressing the underlying root-cause of a problem, instead of enabling partial mitigation.</p> <p><br></p> <p>Finally, in addition to the stride made by our work, future research thrust is required to make TEEs ubiquitous for cloud usage. We propose several such research directions to pursue the essential goal of strong user data protection in cloud machines.</p>