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Security bootstrapping for Internet of Things

Abstract : The demand for internet of Things (IoT) services is increasing exponentially, and a large number of devices are being deployed. However, these devices can represent a serious threat to the security of the deployment network and a potential entry-point when exploited by the adversaries. Thus, there is an imminent need to perform a secure association approach of the IoT objects before being rendered operational on the network of the user. This procedure is referred to as secure bootstrapping, and it primarily guarantees the confidentiality and the integrity of the data exchanges between the user and the devices. Secondly, this process provides an assurance on the identity and the origin of these objects.Due to scalability limitations, the first phase of the bootstrapping process cannot be efficiently conducted using pre-shared security knowledge such as digital certificates. This step is referred to as secure device pairing, and it ensures the establishment of a secure communication channel between the use and the object. The pairing phase uses a symmetric key agreement protocol that is suitable to the resource-constrained nature of these devices. The use of auxiliary channels has been proposed as a way to authenticate the key exchange, but they require a relatively long time and an extensive user involvement to transfer the authentication bits. However, the context-based schemes use the ambient environment to extract a common secret without an extensive user intervention under the requirement of having a secure perimeter during the extraction phase, which is considered a strong security assumption. The second phase of the bootstrapping process is referred to as secure device enrollment, and it aims at avoiding the associating of a malicious IoT object by authenticating its identity. The use of hardware security elements, such as the Physical Unclonable Function (PUF), has been introduced as a promising solution that is suitable for the resource-constraint nature of these devices. A growing number of PUF architectures has been demonstrated mathematically clonable through Machine Learning (ML) modeling techniques. The use of PUF ML models has been recently proposed to authenticate the IoT objects. Nonetheless, the leakage scenario of the PUF model to an adversary due to an insider threat within the organization is not supported by the existing solutions. Hence, the security of these PUF model-based enrollment proposals can be compromised.In this thesis, we study the secure bootstrapping process of resource-constrained devices and we introduce two security schemes:- A hybrid ad-hoc pairing protocol, called COOB, that efficiently combines a state-of-the-art fast context-based scheme with the use of an auxiliary channel. This protocol exploits a nonce exponentiation of the Diffie-Hellman public keys to achieve the temporary secrecy goal needed for the key agreement. Our method provides security even against an attacker that can violate the safe zone requirement, which is not supported by the existing contextual schemes. This security improvement has been formally validated in the symbolic model using the TAMARIN prover.- An enrollment solution that exploits a ML PUF model in the authentication process, called Water-PUF. Our enrollment scheme is based on a specifically designed black-box watermarking technique for PUF models with a binary output response. This procedure prevents an adversary from relying on the watermarked model in question or another derivative model to bypass the authentication. Therefore, any leakage of the watermarked PUF model that is used for the enrollment does not affect the correctness of the protocol. The Water-PUF design is validated by a number of simulations against numerous watermark suppression attacks to assess the robustness of our proposal.
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Submitted on : Monday, July 11, 2022 - 3:34:50 PM
Last modification on : Tuesday, July 12, 2022 - 3:40:19 AM


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  • HAL Id : tel-03719946, version 1



Sameh Khalfaoui. Security bootstrapping for Internet of Things. Cryptography and Security [cs.CR]. Institut Polytechnique de Paris, 2022. English. ⟨NNT : 2022IPPAT023⟩. ⟨tel-03719946⟩



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