Blockchain-enhanced Roots-of-Trust

Establishing a root-of-trust is a key early step in establishing trust throughout the lifecycle of a device, notably by attesting the running software. A key technique is to use hardware security in the form of specialised modules or hardware functions such as TPMs. However, even if a device supports such features, other steps exist that can compromise the overall trust model between devices being manufactured until decommissioning. In this paper, we discuss how blockchains, and smart contracts in particular, can be used to harden the overall security management both in the case of existing hardware enhanced security or when only software attestation is possible

Enhancing Trust in Devices and Transactions of the Internet of Things

With the rise of the Internet of Things (IoT), billions of smart embedded devices will interact frequently.These interactions will produce billions of transactions.With IoT, users can utilize their phones, home appliances, wearables, or any other wireless embedded device to conduct transactions.For example, a smart car and a parking lot can utilize their sensors to negotiate the fees of a parking spot.The success of IoT applications highly depends on the ability of wireless embedded devices to cope with a large number of transactions.However, these devices face significant constraints in terms of memory, computation, and energy capacity.With our work, we target the challenges of accurately recording IoT transactions from resource-constrained devices. We identify three domain-problems: a) malicious software modification, b) non-repudiation of IoT transactions, and c) inability of IoT transactions to include sensors readings and actuators.The motivation comes from two key factors.First, with Internet connectivity, IoT devices are exposed to cyber-attacks.Internet connectivity makes it possible for malicious users to find ways to connect and modify the software of a device.Second, we need to store transactions from IoT devices that are owned or operated by different stakeholders.The thesis includes three papers. In the first paper, we perform an empirical evaluation of Secure Boot on embedded devices.In the second paper, we propose IoTLogBlock, an architecture to record off-line transactions of IoT devices.In the third paper, we propose TinyEVM, an architecture to execute off-chain smart contracts on IoT devices with an ability to include sensor readings and actuators as part of IoT transactions

Secure Sensor Prototype Using Hardware Security Modules and Trusted Execution Environments in a Blockchain Application: Wine Logistic Use Case

The security of Industrial Internet of Things (IIoT) systems is a challenge that needs to be addressed immediately, as the increasing use of new communication paradigms and the abundant use of sensors opens up new opportunities to compromise these types of systems. In this sense, technologies such as Trusted Execution Environments (TEEs) and Hardware Security Modules (HSMs) become crucial for adding new layers of security to IIoT systems, especially to edge nodes that incorporate sensors and perform continuous measurements. These technologies, coupled with new communication paradigms such as Blockchain, offer a high reliability, robustness and good interoperability between them. This paper proposes the design of a secure sensor incorporating the above mentioned technologies—HSMs and a TEE—in a hardware device based on a dual-core architecture. Through this combination of technologies, one of the cores collects the data extracted by the sensors and implements the security mechanisms to guarantee the integrity of these data, while the remaining core is responsible for sending these data through the appropriate communication protocol. This proposed approach fits into the Blockchain networks, which act as an Oracle. Finally, to illustrate the application of this concept, a use case applied to wine logistics is described, where this secure sensor is integrated into a Blockchain that collects data from the storage and transport of barrels, and a performance evaluation of the implemented prototype is providedEuropean Union’s Horizon Europe research and innovation program through the funding project “Cognitive edge-cloud with serverless computing” (EDGELESS) under grant agreement number 101092950FEDER/Junta de Andalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades under Project B-TIC-588-UGR2

Distributed IoT Attestation via Blockchain (Extended Version)

The growing number and nature of Internet of Things (IoT) devices makes these resource-constrained appliances particularly vulnerable and increasingly impactful in their exploitation. Current estimates for the number of connected things commonly reach the tens of billions. The low-cost and limited computational strength of these devices can preclude security features. Additionally, economic forces and a lack of industry expertise in security often contribute to a rush to market with minimal consideration for security implications. It is essential that users of these emerging technologies, from consumers to IT professionals, be able to establish and retain trust in the multitude of diverse and pervasive compute devices that are ever more responsible for our critical infrastructure and personal information. Remote attestation is a well-known technique for building such trust between devices. In standard implementations, a potentially untrustworthy prover attests, using public key infrastructure, to a verifier about its configuration or properties of its current state. Attestation is often performed on an ad hoc basis with little concern for historicity. However, controls and sensors manufactured for the Industrial IoT (IIoT) may be expected to operate for decades. Even in the consumer market, so-called smart things can be expected to outlive their manufacturers. This longevity combined with limited software or firmware patching creates an ideal environment for long-lived zero-day vulnerabilities. Knowing both if a device is vulnerable and if so when it became vulnerable is a management nightmare as IoT deployments scale. For network connected machines, with access to sensitive information and real-world physical controls, maintaining some sense of a device\u27s lifecycle would be insightful. In this paper, we propose a novel attestation architecture, DAN: a distributed attestation network, utilizing blockchain to store and share device information. We present the design of this new attestation architecture, and describe a virtualized simulation, as well as a prototype system chosen to emulate an IoT deployment with a network of Raspberry Pi, Infineon TPMs, and a Hyperledger Fabric blockchain. We discuss the implications and potential challenges of such a network for various applications such as identity management, intrusion detection, forensic audits, and regulatory certification

D2Gen: A Decentralized Device Genome Based Integrity Verification Mechanism for Collaborative Intrusion Detection Systems

Collaborative Intrusion Detection Systems are considered an effective defense mechanism for large, intricate, and multilayered Industrial Internet of Things against many cyberattacks. However, while a Collaborative Intrusion Detection System successfully detects and prevents various attacks, it is possible that an inside attacker performs a malicious act and compromises an Intrusion Detection System node. A compromised node can inflict considerable damage on the whole collaborative network. For instance, when a malicious node gives a false alert of an attack, the other nodes will unnecessarily increase their security and close all of their services, thus, degrading the system’s performance. On the contrary, if the spurious node approves malicious traffic into the system, the other nodes would also be compromised. Therefore, to detect a compromised node in the network, this article introduces a device integrity check mechanism based on “Digital Genome.” In medical science, a genome refers to a set that contains all of the information needed to build and maintain an organism. Based on the same concept, the digital genome is computed over a device’s vital hardware, software, and other components. Hence, if an attacker makes any change in a node’s hardware and software components, the digital genome will change, and the compromised node will be easily detected. It is envisaged that the proposed integrity attestation protocol can be used in diverse Internet of Things and other information technology applications to ensure the legitimate operation of end devices. This study also proffers a comprehensive security and performance analysis of the proposed framework

Self-Reliance for the Internet of Things: Blockchains and Deep Learning on Low-Power IoT Devices

The rise of the Internet of Things (IoT) has transformed common embedded devices from isolated objects to interconnected devices, allowing multiple applications for smart cities, smart logistics, and digital health, to name but a few. These Internet-enabled embedded devices have sensors and actuators interacting in the real world. The IoT interactions produce an enormous amount of data typically stored on cloud services due to the resource limitations of IoT devices. These limitations have made IoT applications highly dependent on cloud services. However, cloud services face several challenges, especially in terms of communication, energy, scalability, and transparency regarding their information storage. In this thesis, we study how to enable the next generation of IoT systems with transaction automation and machine learning capabilities with a reduced reliance on cloud communication. To achieve this, we look into architectures and algorithms for data provenance, automation, and machine learning that are conventionally running on powerful high-end devices. We redesign and tailor these architectures and algorithms to low-power IoT, balancing the computational, energy, and memory requirements.The thesis is divided into three parts:Part I presents an overview of the thesis and states four research questions addressed in later chapters.Part II investigates and demonstrates the feasibility of data provenance and transaction automation with blockchains and smart contracts on IoT devices.Part III investigates and demonstrates the feasibility of deep learning on low-power IoT devices.We provide experimental results for all high-level proposed architectures and methods. Our results show that algorithms of high-end cloud nodes can be tailored to IoT devices, and we quantify the main trade-offs in terms of memory, computation, and energy consumption