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Blockchain-based applications necessitate the authentication of connected devices if they are employed as blockchain oracles. Alongside identity authentication, it is crucial to ensure resistance against tampering, including safeguarding against unauthorized alterations and protection against device counterfeiting or cloning. However, attaining these functionalities becomes more challenging when dealing with resource-constrained devices like low-cost IoT devices. The resources of IoT devices depend on the capabilities of the microcontroller they are built around. Low-cost devices utilize microcontrollers with limited computational power, small memory capacity, and lack advanced features such as a dedicated secure cryptographic chip. This paper proposes a method employing a Physical Unclonable Function (PUF) to authenticate identity and tamper resistance in IoT devices. The suggested PUF relies on a microcontroller's internal pull-up resistor values and, in conjunction with the microcontroller's built-in analog comparator, can also be utilized for device self-checking. A main contribution of this paper is the proposed PUF method which calculates the PUF value as the average value of many single PUF measurements, resulting in a significant increase in accuracy. The proposed PUF has been implemented successfully in a low-cost microcontroller device. Test results demonstrate that the device, specifically the microcontroller chip, can be identified with high accuracy (99.98%), and the proposed PUF method exhibits resistance against probing attempts. [ABSTRACT FROM AUTHOR]

Wearable computing has garnered a lot of attention due to its various advantages, including automatic recognition and categorization of human actions from sensor data. However, wearable computing environments can be fragile to cyber security attacks since adversaries attempt to block, delete, or intercept the exchanged information via insecure communication channels. In addition to cyber security attacks, wearable sensor devices cannot resist physical threats since they are batched in unattended circumstances. Furthermore, existing schemes are not suited for resource-constrained wearable sensor devices with regard to communication and computational costs and are inefficient regarding the verification of multiple sensor devices simultaneously. Thus, we designed an efficient and robust authentication and group–proof scheme using physical unclonable functions (PUFs) for wearable computing, denoted as AGPS-PUFs, to provide high-security and cost-effective efficiency compared to the previous schemes. We evaluated the security of the AGPS-PUF using a formal security analysis, including the ROR Oracle model and AVISPA. We carried out the testbed experiments using MIRACL on Raspberry PI4 and then presented a comparative analysis of the performance between the AGPS-PUF scheme and the previous schemes. Consequently, the AGPS-PUF offers superior security and efficiency than existing schemes and can be applied to practical wearable computing environments. [ABSTRACT FROM AUTHOR]

We introduce a technique to generate and read the digital signature of the networks, channels, and optical devices that possess the fiber-optic pigtails to enhance physical layer security (PLS). Attributing a signature to the networks or devices eases the identification and authentication of networks and systems thus reducing their vulnerability to physical and digital attacks. The signatures are generated using an optical physical unclonable function (OPUF). Considering that OPUFs are established as the most potent anti-counterfeiting tool, the created signatures are robust against malicious attacks such as tampering and cyber attacks. We investigate Rayleigh backscattering signal (RBS) as a strong OPUF to generate reliable signatures. Contrary to other OPUFs that must be fabricated, the RBS-based OPUF is an inherent feature of fibers and can be easily obtained using optical frequency domain reflectometry (OFDR). We evaluate the security of the generated signatures in terms of their robustness against prediction and cloning. We demonstrate the robustness of signatures against digital and physical attacks confirming the unpredictability and unclonability features of the generated signatures. We explore signature cyber security by considering the random structure of the produced signatures. To demonstrate signature reproducibility through repeated measurements, we simulate the signature of a system by adding a random Gaussian white noise to the signal. This model is proposed to address services including security, authentication, identification, and monitoring. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) with cloud services are important functionalities in the latest IoT systems for providing various convenient services. These cloud-enabled IoT environments collect, analyze, and monitor surrounding data, resulting in the most effective handling of large amounts of heterogeneous data. In these environments, secure authentication with a key agreement mechanism is essential to ensure user and data privacy when transmitting data between the cloud server and IoT nodes. In this study, we prove that the previous scheme contains various security threats, and hence cannot guarantee essential security requirements. To overcome these security threats, we propose an improved authentication and key agreement scheme for cloud-enabled IoT using PUF. Furthermore, we evaluate its security by performing informal, formal (mathematical), and simulation analyses using the AVISPA tool and ROR model. The performance and security properties of our scheme are subsequently compared with those of other related schemes. The comparison confirms that our scheme is suitable for a practical cloud-enabled IoT environment because it provides a superior security level and is more efficient than contemporary schemes. [ABSTRACT FROM AUTHOR]

Cybersecurity is a challenge in the utilization of IoT devices. One of the main security functions that we need for IoT devices is authentication. In this work, we used physical unclonable function (PUF) technology to propose a lightweight authentication protocol for IoT devices with long lifetimes. Our focus in this project is a solution for FPGA-based IoT devices. We evaluated the resiliency of our solution against state-of-the-art machine learning attacks. [ABSTRACT FROM AUTHOR]

The Internet of Vehicles (IoV) is a new paradigm for vehicular networks. Using diverse access methods, IoV enables vehicles to connect with their surroundings. However, without data security, IoV settings might be hazardous. Because of the IoV's openness and self-organization, they are prone to malevolent attack. To overcome this problem, this paper proposes a revolutionary blockchain-enabled game theory-based authentication mechanism for securing IoVs. Here, a three layer multi-trusted authorization solution is provided in which authentication of vehicles can be performed from initial entry to movement into different trusted authorities' areas without any delay by the use of Physical Unclonable Functions (PUFs) in the beginning and later through duel gaming, and a dynamic Proof-of-Work (dPoW) consensus mechanism. Formal and informal security analyses justify the framework's credibility in more depth with mathematical proofs. A rigorous comparative study demonstrates that the suggested framework achieves greater security and functionality characteristics and provides lower transaction and computation overhead than many of the available solutions so far. However, these solutions never considered the prime concerns of physical cloning and side-channel attacks. However, the framework in this paper is capable of handling them along with all the other security attacks the previous work can handle. Finally, the suggested framework has been subjected to a blockchain implementation to demonstrate its efficacy with duel gaming to achieve authentication in addition to its capability of using lower burdened blockchain at the physical layer, which current blockchain-based authentication models for IoVs do not support. [ABSTRACT FROM AUTHOR]

The demand for Internet of Things services is increasing exponentially, and consequently a large number of devices are being deployed. To efficiently authenticate these objects, the use of physical unclonable functions (PUFs) has been introduced as a promising solution for the resource-constrained nature of these devices. The use of machine learning PUF models has been recently proposed to authenticate the IoT objects while reducing the storage space requirement for each device. Nonetheless, the use of a mathematically clonable PUFs requires careful design of the enrollment process. Furthermore, the secrecy of the machine learning models used for PUFs and the scenario of leakage of sensitive information to an adversary due to an insider threat within the organization have not been discussed. In this paper, we review the state-of-the-art model-based PUF enrollment protocols. We identity two architectures of enrollment protocols based on the participating entities and the building blocks that are relevant to the security of the authentication procedure. In addition, we discuss their respective weaknesses with respect to insider and outsider threats. Our work serves as a comprehensive overview of the ML PUF-based methods and provides design guidelines for future enrollment protocol designers. [ABSTRACT FROM AUTHOR]

Smart speakers, such as Alexa and Google Home, support daily activities in smart home environments. Even though voice commands enable friction-less interactions, existing financial transaction authorization mechanisms hinder usability. A non-invasive authorization by leveraging presence and light sensors' data is proposed in order to replace invasive procedure through smartphone notification. The Coloured Petri Net model was created for synthetic data generation, and one month data were collected in test bed with real users. Random Forest machine learning models were used for smart home behavior information retrieval. The LSTM prediction model was evaluated while using test bed data, and an open dataset from CASAS. The proposed authorization mechanism is based on Physical Unclonable Function usage as a random number generator seed in a Challenge Response protocol. The simulations indicate that the proposed scheme with specialized autonomous device could halve the total response time for low value financial transactions triggered by voice, from 7.3 to 3.5 s in a non-invasive manner, maintaining authorization security. [ABSTRACT FROM AUTHOR]