KYoT: Self-sovereign IoT Identification with a Physically Unclonable Function

The integration of Internet-of-Things (IoT) and Blockchains (BC) for trusted and decentralized approaches enabled modern use cases, such as supply chain tracing, smart cities, and IoT data marketplaces. For these it is essential to identify reliably IoT devices, since the producer-consumer trust is not guaranteed by a Trusted Third Party (TTP). Therefore, this work proposes a Know Your IoT device platform (KYoT), which enables the self-sovereign identification of IoT devices on the Ethereum BC. KYoT permits manufacturers and device owners to register and verify IoT devices in a self-sovereign fashion, while data storage security is ensured. KYoT deploys an SRAM-based (Static Random Access Memory) Physically Unclonable Function (PUF), which takes advantage of the manufacturing variability of devices’ SRAM chips to derive a unique identifying key for each IoT device. The self-sovereign identification mechanism introduced is based on the ERC 734 and ERC 735 Ethereum identity standards

Proof-of-PUF enabled blockchain: concurrent data and device security for internet-of-energy

A detailed review on the technological aspects of Blockchain and Physical Unclonable Functions (PUFs) is presented in this article. It stipulates an emerging concept of Blockchain that integrates hardware security primitives via PUFs to solve bandwidth, integration, scalability, latency, and energy requirements for the Internet-of-Energy (IoE) systems. This hybrid approach, hereinafter termed as PUFChain, provides device and data provenance which records data origins, history of data generation and processing, and clone-proof device identification and authentication, thus possible to track the sources and reasons of any cyber attack. In addition to this, we review the key areas of design, development, and implementation, which will give us the insight on seamless integration with legacy IoE systems, reliability, cyber resilience, and future research challenges

Key parameters linking cyber-physical trust anchors with embedded internet of things systems

Integration of the Internet of Things (IoT) in the automotive industry has brought benefits as well as security challenges. Significant benefits include enhanced passenger safety and more comprehensive vehicle performance diagnostics. However, current onboard and remote vehicle diagnostics do not include the ability to detect counterfeit parts. A method is needed to verify authentic parts along the automotive supply chain from manufacture through installation and to coordinate part authentication with a secure database. In this study, we develop an architecture for anti-counterfeiting in automotive supply chains. The core of the architecture consists of a cyber-physical trust anchor and authentication mechanisms connected to blockchain-based tracking processes with cloud storage. The key parameters for linking a cyber-physical trust anchor in embedded IoT include identifiers (i.e., serial numbers, special features, hashes), authentication algorithms, blockchain, and sensors. A use case was provided by a two-year long implementation of simple trust anchors and tracking for a coffee supply chain which suggests a low-cost part authentication strategy could be successfully applied to vehicles. The challenge is authenticating parts not normally connected to main vehicle communication networks. Therefore, we advance the coffee bean model with an acoustical sensor to differentiate between authentic and counterfeit tires onboard the vehicle. The workload of secure supply chain development can be shared with the development of the connected autonomous vehicle networks, as the fleet performance is degraded by vehicles with questionable replacement parts of uncertain reliability

From truth to trust: the impact of blockchain traceability on trust in product authenticity

In the global marketplace, customers are increasingly unaware of the source, provenance, and authenticity of products. Early research has shown that the introduction of blockchain technology into the supply chain area can make it more transparent and trustworthy. As a platform that supports distributed, cryptographically secure, auditable transactions, blockchain has expanded from the domain of digital cryptocurrency into the domain of physical asset provenance and ownership tracking and tracing. This research examines blockchain support of trust in product authenticity adopting a two-paper dissertation format. In the first conceptual paper, I develop a conceptual framework on blockchain technology\u27s unique features and characteristics and how it can boost trust in product authenticity. The second paper adopts the conceptual framework to test through a vignette experiment the effects of blockchain traceability, product identification, and the interaction between them on trust in product origin authenticity. Academics can use this research to develop new instruments to inform practice about how blockchain can boost trust in product authenticity. Results from this study can inform managers considering investments into blockchain solutions and unique product identification as a customer product authenticity, brand protection, or anti-counterfeiting strategy

Lightweight mutual authentication and privacy preservation schemes for IOT systems.

Internet of Things (IoT) presents a holistic and transformative approach for providing services in different domains. IoT creates an atmosphere of interaction between humans and the surrounding physical world through various technologies such as sensors, actuators, and the cloud. Theoretically, when everything is connected, everything is at risk. The rapid growth of IoT with the heterogeneous devices that are connected to the Internet generates new challenges in protecting and preserving user’s privacy and ensuring the security of our lives. IoT systems face considerable challenges in deploying robust authentication protocols because some of the IoT devices are resource-constrained with limited computation and storage capabilities to implement the currently available authentication mechanism that employs computationally expensive functions. The limited capabilities of IoT devices raise significant security and privacy concerns, such as ensuring personal information confidentiality and integrity and establishing end-to-end authentication and secret key generation between the communicating device to guarantee secure communication among the communicating devices. The ubiquity nature of the IoT device provides adversaries more attack surfaces which can lead to tragic consequences that can negatively impact our everyday connected lives. According to [1], authentication and privacy protection are essential security requirements. Therefore, there is a critical need to address these rising security and privacy concerns to ensure IoT systems\u27 safety. This dissertation identifies gaps in the literature and presents new mutual authentication and privacy preservation schemes that fit the needs of resource-constrained devices to improve IoT security and privacy against common attacks. This research enhances IoT security and privacy by introducing lightweight mutual authentication and privacy preservation schemes for IoT based on hardware biometrics using PUF, Chained hash PUF, dynamic identities, and user’s static and continuous biometrics. The communicating parties can anonymously communicate and mutually authenticate each other and locally establish a session key using dynamic identities to ensure the user’s unlinkability and untraceability. Furthermore, virtual domain segregation is implemented to apply security policies between nodes. The chained-hash PUF mechanism technique is implemented as a way to verify the sender’s identity. At first, this dissertation presents a framework called “A Lightweight Mutual Authentication and Privacy-Preservation framework for IoT Systems” and this framework is considered the foundation of all presented schemes. The proposed framework integrates software and hardware-based security approaches that satisfy the NIST IoT security requirements for data protection and device identification. Also, this dissertation presents an architecture called “PUF Hierarchal Distributed Architecture” (PHDA), which is used to perform the device name resolution. Based on the proposed framework and PUF architecture, three lightweight privacy-preserving and mutual authentication schemes are presented. The Three different schemes are introduced to accommodate both stationary and mobile IoT devices as well as local and distributed nodes. The first scheme is designed for the smart homes domain, where the IoT devices are stationary, and the controller node is local. In this scheme, there is direct communication between the IoT nodes and the controller node. Establishing mutual authentication does not require the cloud service\u27s involvement to reduce the system latency and offload the cloud traffic. The second scheme is designed for the industrial IoT domain and used smart poultry farms as a use case of the Industrial IoT (IIoT) domain. In the second scheme, the IoT devices are stationary, and the controller nodes are hierarchical and distributed, supported by machine-to-machine (M2M) communication. The third scheme is designed for smart cities and used IoV fleet vehicles as a use case of the smart cities domain. During the roaming service, the mutual authentication process between a vehicle and the distributed controller nodes represented by the Roadside Units (RSUs) is completed through the cloud service that stores all vehicle\u27s security credentials. After that, when a vehicle moves to the proximity of a new RSU under the same administrative authority of the most recently visited RSU, the two RSUs can cooperate to verify the vehicle\u27s legitimacy. Also, the third scheme supports driver static and continuous authentication as a driver monitoring system for the sake of both road and driver safety. The security of the proposed schemes is evaluated and simulated using two different methods: security analysis and performance analysis. The security analysis is implemented through formal security analysis and informal security analysis. The formal analysis uses the Burrows–Abadi–Needham logic (BAN) and model-checking using the automated validation of Internet security protocols and applications (AVISPA) toolkit. The informal security analysis is completed by: (1) investigating the robustness of the proposed schemes against the well-known security attacks and analyze its satisfaction with the main security properties; and (2) comparing the proposed schemes with the other existing authentication schemes considering their resistance to the well-known attacks and their satisfaction with the main security requirements. Both the formal and informal security analyses complement each other. The performance evaluation is conducted by analyzing and comparing the overhead and efficiency of the proposed schemes with other related schemes from the literature. The results showed that the proposed schemes achieve all security goals and, simultaneously, efficiently and satisfy the needs of the resource-constrained IoT devices

Robust Federated Learning for execution time-based device model identification under label-flipping attack

The computing device deployment explosion experienced in recent years, motivated by the advances of technologies such as Internet-of-Things (IoT) and 5G, has led to a global scenario with increasing cybersecurity risks and threats. Among them, device spoofing and impersonation cyberattacks stand out due to their impact and, usually, low complexity required to be launched. To solve this issue, several solutions have emerged to identify device models and types based on the combination of behavioral fingerprinting and Machine/Deep Learning (ML/DL) techniques. However, these solutions are not appropriate for scenarios where data privacy and protection are a must, as they require data centralization for processing. In this context, newer approaches such as Federated Learning (FL) have not been fully explored yet, especially when malicious clients are present in the scenario setup. The present work analyzes and compares the device model identification performance of a centralized DL model with an FL one while using execution time-based events. For experimental purposes, a dataset containing execution-time features of 55 Raspberry Pis belonging to four different models has been collected and published. Using this dataset, the proposed solution achieved 0.9999 accuracy in both setups, centralized and federated, showing no performance decrease while preserving data privacy. Later, the impact of a label-flipping attack during the federated model training is evaluated using several aggregation mechanisms as countermeasures. Zeno and coordinate-wise median aggregation show the best performance, although their performance greatly degrades when the percentage of fully malicious clients (all training samples poisoned) grows over 50%

Security and Privacy for Modern Wireless Communication Systems

The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks

Using Distributed Ledger Technologies to Support Complex Supply Chains

The concept of blockchain, as part of distributed ledger technologies, has gained a lot of interest recently, especially in cryptocurrencies. With the addition of other technical capabilities, e.g., smart contracts and oracles, this interest has spread to other areas as well and affects a wide variety of business processes such as supply chain processes. However, in research, the wide variety of processes finds inadequate consideration to date. In this research paper, we provide an overview of the state of the art of distributed ledger technologies in supply chains and point out future research topics. Therefore, we conducted a structured literature review, systematized potential application areas in supply chain processes, and showed that research gaps exist. To address the research gaps, we derived open research questions, whereby conducting design studies to deal with the practical problems in the application areas plays a central role