Authentication For Internet Of Things Research Articles

MAG-PUFs: Authenticating IoT devices via electromagnetic physical unclonable functions and deep learning

The challenge of authenticating Internet of Things (IoT) devices, particularly in low-cost deployments with constrained nodes that struggle with dynamic re-keying solutions, renders these devices susceptible to various attacks. This paper introduces a robust alternative mitigation strategy based on Physical-Layer Authentication (PLA), which leverages the intrinsic physical layer characteristics of IoT devices. These unique imperfections, stemming from the manufacturing process of IoT electronic integrated circuits (ICs), are difficult to replicate or falsify and vary with each function executed by the IoT device. We propose a novel lightweight authentication scheme, MAG-PUFs, that uses the unintentional Electromagnetic (EM) emissions from IoT devices as Physical Unclonable Functions (PUFs). MAG-PUFs operate by collecting these unintentional EM emissions during the execution of pre-defined reference functions by the IoT devices. The authentication is achieved by matching these emissions with profiles recorded at the time of enrollment, using state-of-the-art Deep Learning (DL) approaches such as Neural Networks (NN) and Autoencoders. Notably, MAG-PUFs offer compelling advantages: (i) it preserves privacy, as it does not require direct access to the IoT devices; and, (ii) it provides unique flexibility, permitting the selection of numerous and varied reference functions. We rigorously evaluated MAG-PUFs using 25 Arduino devices and a diverse set of 325 reference function classes. Employing a DL framework, we achieved a minimum authentication F1-Score of 0.99. Furthermore, the scheme’s efficacy in detecting impostor EM emissions was also affirmed, achieving a minimum F1-Score of 0.99. We also compared our solution to other solutions in the literature, showing its remarkable performance. Finally, we discussed code obfuscation techniques and the impact of Radio Frequency (RF) interference on the IoT authentication process.

MWIoTAuth: Multi-wearable data-driven implicit IoT authentication

Due to the advancement of wearables and the Internet of Things (IoT), end-users are using a range of wearables, such as smartwatches, fitness bands, and smart glasses, to receive a range of services, e.g., bank transactions and access several physical objects, e.g., smart cars/homes. While wearables collect various information, e.g., physiological and behavioral data of an IoT end-user, to provide different services, market-wearables often have no authentication or have knowledge-based authentications. This limitation brings additional security challenges. However, similar types of data, e.g., heart rate (source: both Wellue and Fitbit), or different types of data, e.g., oxygen saturation values (source: Wellue) and calorie burn (source: Fitbit), obtained from multiple IoT-connected wearables could contain complementary information (due to positional variation of different wearables on different body parts), which can be helpful in uniquely identifying a user. Therefore, in this work, we propose an implicit IoT end-user authentication (mWIoTAuth) approach utilizing data from two market wearables, i.e., Wellue (providing heart rate and oxygen saturation values) and Fitbit (providing heart rate, calorie burn, and step count). From our detailed analysis of a 2-phase study conducted with two separate cohorts of 40 subjects wearing Wellue and Fitbit in their daily life for continuous 8 h, we find that models developed from two wearables have 5%–14% higher accuracy and F1 score compared to the single-wearable models. These findings show the promise to develop multi-wearable implicit authentication for end-users to secure the IoT world accessible via wearables.

All-silicon multidimensionally-encoded optical physical unclonable functions for integrated circuit anti-counterfeiting

Integrated circuit anti-counterfeiting based on optical physical unclonable functions (PUFs) plays a crucial role in guaranteeing secure identification and authentication for Internet of Things (IoT) devices. While considerable efforts have been devoted to exploring optical PUFs, two critical challenges remain: incompatibility with the complementary metal-oxide-semiconductor (CMOS) technology and limited information entropy. Here, we demonstrate all-silicon multidimensionally-encoded optical PUFs fabricated by integrating silicon (Si) metasurface and erbium-doped Si quantum dots (Er-Si QDs) with a CMOS-compatible procedure. Five in-situ optical responses have been manifested within a single pixel, rendering an ultrahigh information entropy of 2.32 bits/pixel. The position-dependent optical responses originate from the position-dependent radiation field and Purcell effect. Our evaluation highlights their potential in IoT security through advanced metrics like bit uniformity, similarity, intra- and inter-Hamming distance, false-acceptance and rejection rates, and encoding capacity. We finally demonstrate the implementation of efficient lightweight mutual authentication protocols for IoT applications by using the all-Si multidimensionally-encoded optical PUFs.

Provably Secure ECC-Based Anonymous Authentication and Key Agreement for IoT

With the rise of the Internet of Things (IoT), maintaining data confidentiality and protecting user privacy have become increasingly challenging. End devices in the IoT are often deployed in unattended environments and connected to open networks, making them vulnerable to physical tampering and other security attacks. Different authentication key agreement (AKA) schemes have been used in practice; several of them do not cover the necessary security features or are incompatible with resource-constrained end devices. Their security proofs have been performed under the Random-Oracle model. We present an AKA protocol for end devices and servers. The proposal leverages the ECC-based key exchange mechanism and one-way hash function-based message authentication method to achieve mutual authentication, user anonymity, and forward security. A formal security proof of the proposed scheme is performed under the standard model and the eCK model with the elliptic curve encryption computational assumptions, and formal verification is performed with ProVerif. According to the performance comparison, it is revealed that the proposed scheme offers user anonymity, perfect forward security, and mutual authentication, and resists typical attacks such as ephemeral secret leakage attacks, impersonation attacks, man-in-the-middle attacks, and key compromise impersonation attacks. Moreover, the proposed scheme has the lowest computational and communication overhead compared to existing schemes.

An All-Inclusive Taxonomy and Critical Review of Blockchain-Assisted Authentication and Session Key Generation Protocols for IoT

Authentication and Session Key Generation Protocols (SKGPs) play an essential role in securing the communication channels of connected Internet of Things (IoT) devices. Recently, through blockchain integration, scholars have tried to enhance the security and applicability of SKGPs. In brief, blockchain is a distributed ledger technology that can provide interesting features such as immutability, transparency, and accountability without any need for the active participation of trusted parties. This survey presents a comprehensive critical review of blockchain-assisted authentication and SKGPs, suggested for different IoT domains, including Internet of Vehicles, Internet of Drones, and Industrial IoT. Our survey categorizes existing schemes based on several criteria, including IoT application domains, security aspects, and blockchain components. By presenting an unbiased critical review and taxonomy of protocols, we aim to clarify the key challenges. Our review will specifically indicate what properties authors gained or lost through the integration of blockchain. To our best knowledge, this survey is the only one that offers all prerequisites for interested readers in blockchain-integrated SKGPs, such as security features and attacks, attack models, verification tools, blockchain types, blockchain platforms, and consensus mechanisms. Further, our survey elaborates existing research gaps in blockchain-assisted SKGPs. In doing so, we aim to guide future research in this field and provide researchers with the essential information they require.

An Elliptic Curve Menezes–Qu–Vanston-Based Authentication and Encryption Protocol for IoT

The exponential growth of the Internet of Things (IoT) has led to a surge in data generation, critical for business decisions. Ensuring data authenticity and integrity over unsecured channels is vital, especially due to potential catastrophic consequences of tampered data. However, IoT’s resource constraints and heterogeneous ecosystem present unique security challenges. Traditional public key infrastructure offers strong security but is resource intensive, while existing cloud-based solutions lack comprehensive security and rise to latency and unwanted wastage of energy. In this paper, we propose a universal authentication scheme using edge computing, incorporating fully hashed Elliptic Curve Menezes–Qu–Vanstone (ECMQV) and PUF. This approach provides a scalable and reliable solution. It also provides security against active attacks, addressing man-in-the-middle and impersonation threats. Experimental validation on a Zybo board confirms its effectiveness, offering a robust security solution for the IoT landscape.

A Trusted Internet of Things Access Scheme for Cloud Edge Collaboration

With the rapid popularization of current Internet of Things (IoT) technology and 5G networks, as well as the continuous updating of new service lifestyles and businesses, the era of big data processing for the IoT has arrived. However, centralizing all data for processing in the cloud can lead to issues such as communication latency and privacy breaches. To solve these problems, edge computing, as a new network architecture close to terminal data sources and supporting low latency services, has gradually emerged. In this context, cloud edge collaborative computing has become an important network architecture. With the changing security requirements and communication methods of cloud edge collaborative network architecture, traditional authentication key agreement protocols are no longer applicable. Therefore, a new IoT authentication and key agreement protocol needs to be designed to solve this problem. This study proposes an IoT accessible solution for cloud edge collaboration. This scheme adopts a chaotic mapping algorithm to achieve efficient authentication. It ensures the anonymity and untraceability of users. Following this, we conducted strict security verification using BAN logic and Scyther tools. Through experimental comparative analysis, the research results show that the protocol performs better than other schemes while ensuring security. This indicates that the protocol can achieve efficient authentication and key negotiation in cloud edge collaborative network architecture, providing a secure and reliable solution for the accessibility of the IoT.

Exploring Authentication Paradigms in the Internet of Things: A Comprehensive Scoping Review

In the rapidly expanding domain of the Internet of Things (IoT), ensuring the implementation of robust security measures such as authentication has become paramount to safeguarding sensitive data and maintaining the integrity of connected devices. Symmetry in the IoT commonly denotes the uniformity or equilibrium in data distribution and processing across devices or nodes in a network. Leveraging symmetric patterns can enhance the robustness and scalability of IoT authentication. This scoping review aims to provide a comprehensive overview of recent developments in authentication techniques within the IoT paradigm. It subsequently presents recent research on various IoT authentication schemes, organized around several key research questions. The objective is to decipher the intricacies associated with authentication in the IoT by employing a multi-criteria classification approach. This involves a comprehensive analysis of existing authentication protocols, delineating their respective advantages and disadvantages, and gaining insights into the associated security concerns. The research questions highlighted in the review aim to probe the present scenario of authentication systems utilized in IoT, with a focus on identifying trends and discerning shifts. This review synthesizes insights from scholarly articles to provide a roadmap for future research in IoT authentication. It functions as a valuable resource for establishing theoretical foundations and provides practical implications applicable to practitioners, policymakers, and researchers alike. By elucidating the intricacies of IoT authentication, this review cultivates a profound understanding of the transformative potential and the multifaceted challenges. It establishes the foundation for resilient security measures essential for the sustainable growth of the Internet of Things.

Authentication in Internet of Things, protocols, attacks, and open issues: a systematic literature review

Authentication in Internet of Things, protocols, attacks, and open issues: a systematic literature review

Enhancing Security of 5G-Enabled IoT Systems through Advanced Authentication Mechanisms: A Multifaceted Approach

The Internet of Things (IoT) has revolutionized communication among devices, offering unprecedented efficiency and convenience. However, the widespread adoption of IoT has raised significant security concerns, emphasizing the need for robust security measures. This study focuses on the crucial aspect of authentication within the layered architecture of IoT systems. Authentication is foundational to the architecture, ensuring the availability, security, and integrity of IoT services and data. The research evaluates the current state of IoT authentication techniques, highlighting limitations in conventional solutions. To address these shortcomings, an advanced authentication framework is proposed, incorporating cutting-edge technologies such as blockchain, artificial intelligence, and biometrics. The framework employs biometric data for a dynamic and adaptive authentication process, enhancing security and accuracy in user and device identification. Blockchain technology is integrated to establish a decentralized and tamper-resistant identity management system, reducing the risk of unauthorized access and data manipulation. Artificial intelligence continuously adapts authentication processes based on behavioural patterns, bolstering the system's resilience against evolving cyber threats. The study also discusses the practical application of the proposed authentication system, considering resource limitations in IoT devices. It provides insights into the efficiency, scalability, and interoperability of the suggested solution within various IoT ecosystems. The research contributes to the ongoing discourse on IoT security by thoroughly examining enhanced authentication procedures. Organizations can fortify their IoT deployments against a growing array of cyber threats by prioritizing advanced authentication within a layered design, fostering a more secure and reliable IoT ecosystem. Additionally, the study presents a comprehensive overview of the state-of-the-art security in IoT, exploring various designs, enabling technologies, and protocols. It delves into security challenges at each architectural tier, providing an in-depth analysis of attack taxonomies and advanced defenses. The article serves as a valuable resource for researchers and academics in the IoT sector, offering a detailed survey of architectural security, identification of challenges, resolution strategies, and insights into the evolving landscape of IoT. The study provides a comprehensive survey of IoT architectural security, identifying challenges, proposing resolutions, and highlighting changes in the IoT domain. This research aims to enhance the accuracy by 80% of IoT security, fostering a more secure and reliable IoT ecosystem.

Single-board device individual authentication based on hardware performance and autoencoder transformer models

The proliferation of the Internet of Things (IoT) has led to the emergence of crowdsensing applications, where a multitude of interconnected devices collaboratively collect and analyze data. Ensuring the authenticity and integrity of the data collected by these devices is crucial for reliable decision-making and maintaining trust in the system. Traditional authentication methods are often vulnerable to attacks or can be easily duplicated, posing challenges to securing crowdsensing applications. Besides, current solutions leveraging device behavior are mostly focused on device identification, which is a simpler task than authentication. To address these issues, an individual IoT device authentication framework based on hardware behavior fingerprinting and Transformer autoencoders is proposed in this work. To support the design, a threat model details the security problems faced when performing hardware-based authentication in IoT. This solution leverages the inherent imperfections and variations in IoT device hardware to differentiate between devices with identical specifications. By monitoring and analyzing the behavior of key hardware components, such as the CPU, GPU, RAM, and Storage on devices, unique fingerprints for each device are created. The performance samples are considered as time series data and used to train outlier detection transformer models, one per device and aiming to model its normal data distribution. Then, the framework is validated within a spectrum crowdsensing system leveraging Raspberry Pi devices. After a pool of experiments, the model from each device is able to individually authenticate it between the 45 devices employed for validation. An average True Positive Rate (TPR) of 0.74±0.13 and an average maximum False Positive Rate (FPR) of 0.06±0.09 demonstrate the effectiveness of this approach in enhancing authentication, security, and trust in crowdsensing applications.

Implicit IoT authentication using on-phone ANN models and breathing data

The expansionist nature of the Internet of Things (IoT) with smart sensing has integrated smart technology into the fabric of daily life. From digital health check-ups and personal property access to financial transactions are conveniently performed with the help of smart technologies, such as smart wearables. Therefore, the security of these devices and the entire IoT-connected cyber–physical space accessible through smart wearables has become a burning question. While the current knowledge-based security approaches (e.g., pattern locks, passwords, or PINs) cause a recall burden to the user, conventional one-time biometric-based methods are not adaptable to the tiny wearables. Therefore, this work presents an implicit IoT authentication utilizing breathing patterns to verify a user through continuous microphone sensing. The core piece of this work is the Breathing data-driven TensorFlow Lite framework-supported on-phone authentication application (i.e., BTL Auth app), which consists of an audio processing pipeline (to perform filtering and feature computation) and an artificial neural network model to verify the target user. From our detailed analysis of sequential and non-sequential neural networks trained with static and dynamic audio features, we can achieve an average accuracy of up to .93 when authenticating a user from breathing patterns. Finally, the BTL Auth app needs around 64 KB of memory and 5 s to verify the user.

Joint Heterogeneous PUF-Based Security-Enhanced IoT Authentication

This study proposes a novel authentication scheme that provides enhanced security for Internet of Things (IoT) applications by integrating a radio frequency (RF) physical unclonable function (PUF) with a device PUF. While traditional PUF-based authentication methods are lightweight, they are vulnerable to replay attacks because challenge-response pairs (CRPs) are exposed to adversaries over wireless channels. To prevent CRP exposure, we propose a joint scheme that integrates the physical layer features of wireless channels with those of the device PUF. Our authentication process consists of two stages: an enrollment stage and an authentication stage. During the enrollment stage, the physical features of the static random-access memory (SRAM) in the IoT devices are shared with the server, which generates a hashing model using the amplitudes of channel state information (CSI) as the RF-PUF and transfers it to the IoT device. In the authentication stage, the server and IoT devices exchange pilot signals to estimate the shared CSI. Both parties then generate the challenge information using the hashing model, which depends on the CSI. The challenge information is mapped to the response of the device PUF as the cryptographic key. If both parties are legitimate, the proposed model is updated through CSI amplitudes. We evaluated the proposed authentication protocol using a testbed based on Raspberry Pi and acquired a CSI dataset and SRAM-PUF using universal software radio peripheral and Arduino, respectively. Numerical results demonstrate that our method effectively defends against diverse attacks, even in critical CRP exposure scenarios.

Dingo algorithm-based forwarder selection and huffman coding to improve authentication

<span>In wireless sensor network (WSN), the high volume of observe and transmitted data among sensor nodes make it requires to maintain the security. Even though numerous secure data transmission approaches designed over a network, an inadequate resource and the complex environment cause not able to used in WSNs. Moreover, secure data communication is a big challenging problem in WSNs especially for the military application. This paper proposes a dingo algorithm-based forwarder selection and huffman coding (DAHC) to improve authentication in internet of things (IoT) WSN. Initially, it detects the anomalous nodes by applying support vector machine (SVM) algorithm based on sensor node energy, node selfishness, and signal to noise ratio (SNR). Next, we using the dingo algorithm to select the forwarder node. This dingo algorithm computes the fitness function based on node degreee, node distance and node energy. Finally, the huffman coding to provide end to end authentication established on node energy from sender to receiver. During data transmission, the huffman coding to build the binary hop count value, it improves the authentication in the WSN. Performance results specify that this approach enhances the detection ratio and throughput.</span>

A secure and efficient AKE scheme for IoT devices using PUF and cancellable biometrics

As the Internet of Things (IoT) continues to grow, there is an increasing need for secure and efficient authentication protocols for IoT devices. The design of IoT authentication schemes requires several essential features such as real two-factor security, Key Compromise Impersonation (KCI) resilience, and Perfect Forward Secrecy (PFS). In this paper, we perform a cryptanalysis of two state-of-the-art PUF-based Authentication and Key Exchange(AKE) schemes and show informally how they fail to achieve real two-factor security, PFS and suffer KCI, user traceability, and database attacks. To address these limitations, we propose an improved two-party AKE scheme between the user-device and the server based on a combination of a physically unclonable function (PUF) and a cancellable biometric (CB). Our proposed scheme provides real two-factor security, KCI resilience, PFS, and user untraceability, achieving all desired properties based on the Computational Diffie–Hellman (CDH) assumption in the random oracle model. To establish the security of our proposed scheme, we utilize formal analysis methods, including the RoR model and ProVerif, and perform an informal security analysis to demonstrate its resilience against potential attacks. Additionally, we analyze the computational and communication performance of our proposed scheme against related PUF-based schemes. Our proposed scheme exhibits a computational and communicational feasible overhead for deployment in the context of the IoT, with a lower magnitude than most previous related PUF-based schemes.Overall, our proposed scheme provides an efficient and secure authentication solution for IoT devices, with improved security and reduced communication overhead compared to previous PUF-based schemes. The combination of formal and informal analysis provides robust evidence of the strength and reliability of our proposed scheme, and its scalability makes it a viable solution for large-scale IoT deployments.

BTAA: Blockchain and TEE-Assisted Authentication for IoT Systems

In recent years, Internet of Things (IoT) technology has gained a lot of attention. With the development of IoT technology, it comes the need for devices with different trust domains to interact and collaborate. In order to protect the security and reliability of the communication between devices in different trust domains, it raises the concerns about the the technology of cross-domain authentication. Traditional cross-domain authentication methods may lead to heavy key management overhead or depend on trusted third parties, while existing blockchain-based cross-domain authentication schemes do not prevent the possibility of mischief by malicious domain managers. In this paper, we design an efficient Blockchain and TEE assisted secure device authentication scheme for cross-domain IoT system, called BTAA. Our solution solves the problem that managers are not fully trusted, which in turns protect the security and reliability in the blockchain-based cross-domain communication. Specifically, blockchain is introduced to build trust between different domains, the identity-based signatures are used to verify the identity information of devices and the TEE is introduced to prevent the possibility of mischief by domain managers. Finally, our experiments show that the introduction of TEE has greatly improved system security with a low efficiency reduction, which proves that our scheme can achieve highly secure distributed IoT authentication..

Blockchain-Assisted Secure Intra/Inter-Domain Authorization and Authentication for Internet of Things

Blockchain-Assisted Secure Intra/Inter-Domain Authorization and Authentication for Internet of Things

Design and Implementation of Efficient IoT Authentication Schemes for MQTT 5.0

<p>MQTT (Message Queue Telemetry Transport) is one of the most popular Internet of Things (IoT) communication protocols, owing to its lightweight and easiness to use. The previous MQTT standards (including version 3.1 and its precedents) do not provide proper security functions; instead, they assume the adoption of SSL/TLS in the underlying layer. However, it not only incurs larger overhead but also hinders the development of suitable authentication/confidentiality protection to suit various MQTT deployment scenarios. The newest MQTT standard called MQTT 5.0 responds to these challenges by supporting the Enhanced Authentication framework in which designers can design and implement any secure authentication mechanisms within the framework. This paper designs and implements two efficient authentication protocols, using the MQTT 5.0 Enhanced Authentication framework. One is simple challenge-response authentication scheme, and the other is an anonymous challenge-response authentication scheme. We extend HiveMQ platform to implement the schemes and evaluate the performance. The results show that the proposed schemes demand only hundred few more milliseconds to achieve much robust authentication, compared to the simple identity-password authentication.</p> <p> </p>

A Lightweight, Secure Big Data-Based Authentication and Key-Agreement Scheme for IoT with Revocability

With the rapid development of Internet of Things (IoT), designing a secure two-factor authentication scheme for IoT is becoming increasingly demanding. Two-factor protocols are deployed to achieve a higher security level than single-factor protocols. Given the resource constraints of IoT devices, other factors such as biometrics are ruled out as additional authentication factors due to their large overhead. Smart cards are also prone to side-channel attacks. Therefore, historical big data have gained interest recently as a novel authentication factor in IoT. In this paper, we show that existing big data-based schemes fail to achieve their claimed security properties such as perfect forward secrecy (PFS), key compromise impersonation (KCI) resilience, and server compromise impersonation (SCI) resilience. Assuming a real strong attacker rather than a weak one, we show that previous schemes not only fail to provide KCI and SCI but also do not provide real two-factor security and revocability and suffer inside attack. Then, we propose our novel scheme which can indeed provide real two-factor security, PFS, KCI, and inside attack resilience and revocability of the client. Furthermore, our performance analysis shows that our scheme has reduced modular exponentiation operation and multiplication for both the client and the server compared to Liu et al.’s scheme which reduces the execution time by one third for security levels of λ = 128 . Moreover, in order to cope with the potential threat of quantum computers, we suggest using lightweight XMSS signature schemes which provide the desired security properties with λ = 128 bit postquantum security. Finally, we prove the security of our proposed scheme formally using both the real-or-random model and the ProVerif analysis tool.

Trust and Voice Biometrics Authentication for Internet of Things

In recent years, IoT adoption has been higher, and this causes lots of security concerns. One of the fundamental security concerns in IoT adoption is the question, “Are you who you say you are?” Thus, authentication forms the gateway for a secure communication system with IoT. So far, the human voice is one of the most natural, non-intrusive, and convenient behavioural biometric factors compared to other biometric authentication methods. Despite the non-intrusive characteristics of voice as a biometric authentication factor when accessing IoT technologies, there is a concern of a general societal trust and distrust with IoT technology and the risk of theft of users' data and imitation. This study derived a realistic trust evaluation model that incorporates privacy, reliability, security, usability, safety, and availability factors into a trust vector for a flexible measurement of trust in the user accessing IoT technologies.