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Physical Unclonable Functions (PUFs) are widely researched in the field of security because of their unique, robust, and reliable nature, PUFs are considered device-specific root keys that are hard to duplicate. There are many variants of PUFs that are being studied and implemented including hardware and software PUFs. Though PUFs are believed to be secure and reliable, they are not without challenges of their own. The efficient performance of PUF depends on various environmental factors, which leads to inefficiency. Bit flipping is one such problem that can bring down the reliability of the PUF. Memory-based PUFs are prone to unavoidable bit flips occurring in the hardware, similarly, sensor-based PUFs are prone to bit flips occurring due to temperature variation. The number of errors in the PUF response must be minimized to improve the reliability of the PUF in security applications. In this research we explore the Machine Learning (ML) model based on K-mer sequencing to detect and correct the bit flips in the PUFs, hence fortifying the PUF-based secure authentication system for authentication and authorization of Edge Data Centers (EDC) in a Collaborative Edge Computing (CEC) Environment. 

This paper proposes a new controlled Physical Unclonable Function (PUF), Veda-PUF, which uses an algorithm for pre-processing and post-processing the input and output of PUF to increase the security of the keys generated in Internet-of-Things (IoT) devices. The key size of the PUF can be increased using the proposed protocol without compromising the integrity of the keys generated. The uniqueness of the generated keys was 50 % and the reliability of the keys generated is 99.9 % which are close to the ideal values. The proposed control algorithm also increases the uniqueness and reliability of the PUF keys after processing. This increases the number of PUF keys that can be used for various applications. 

The simplicity, low cost, and scalability of Internet of Things (IoT) devices have led researchers to study their applications in a wide range of areas such as Healthcare, Transportation, and Agriculture. IoT devices help farmers to monitor the conditions in a field. These are connected to edge devices for real-time analysis. The edge servers send commands to actuators in the farm directly, without human intervention. At the same time, security vulnerabilities are a big concern, concomitant with the increasing utilization of IoT devices. If the duplication of an IoT device occurs and attackers gain access to the system, then the integrity of the entire ecosystem will be at stake, regardless of the application domain. This paper presents a Physical Unclonable Function (PUF) based hardware security primitive for the authentication of Internet of Agro-Things (IoAT) devices. The proposed security scheme has been prototyped with a testbed evaluation. An arbiter PUF module has been used for the validation of the proposed scheme. The PUF based security primitive is lightweight, scalable, and robust as it mainly depends on inherent manufacturing variations, thereby ensuring no chance for the duplication of IoT devices. 

his is an extended abstract for the research demo of a novel hardware-assisted scalable blockchain called PUFChain. This work presents a scalable energy-efficient private/permissioned blockchain (integrated with Physical Unclonable Functions or PUFs) which can bedeployed in the IoT. PUFs have a multiple of roles in the blockchain: higher security, lower latency, and reduced energy consumption. Experimental validations of PUFChain show a transaction time of 198ms.To the best of authors knowledge this is the first ever work that presents a comprehensive framework integrating PUFs in a blockchain. 

This is an extended abstract for Research Demo Session based on our published article [1]. One of the major vulnerabilities of the Internet of Medical Things (IoMT) devices is identity spoofing. As a solution, a device authentication protocol is presented in this paper which authenticates the devices in the network without storing the information in the memory.Physical Unclonable Functions (PUFs) are used for giving a unique identity to each device present in the network and for being authenticated when transmitting the data to the serve