Secure Two-Factor Authentication for IoT Device

Pahlevi, Rizka Reza; Suryani, Vera; Nuha, Hilal Hudan; Yasirandi, Rahmat

doi:10.1109/icoict55009.2022.9914866

“…RFID: In the realm of RFID, research papers [65][66][67][68] centre around the authentication and authorization of RFID devices, particularly in supply chain management, access control, and asset tracking. This domain contributes 6% to IoT-based authentication technologies, underscoring its significance in diverse applications.…”

Section: Types Of Publication Channelsmentioning

confidence: 99%

“…This section will delve into the five contribution approaches for each domain, and they have been mapped in Table 7. Among the most popular problems involves addressing the issue of computational constraints associated with authentication processes in IoT, which leads to the proposal of a lightweight authentication framework [7,44,55,59,61,64,65,67,70], especially when integrating blockchain technology [80]. Another problem arose from the risk to personal data (or data privacy), which requires a secure communication protocol to protect the privacy and reliability of IoT communications [10,30,32,33,50,52,53,65,71,77].…”

Section: Rq4 What Is the Contribution Of Authentication In Iot System?mentioning

confidence: 99%

“…This multi-entity authentication approach adds an extra layer of security to RFIDbased IoT systems. Pahlevi [67] explores a secure mutual authentication protocol utilizing two-factor RFID and fingerprint authentication through the MQTT protocol. The protocol attains optimal FAR and FRR at an 80% threshold, demonstrating robust performance with an Equal Error Rate (ERR) of approximately 59.5%.…”

Section: Rfidmentioning

confidence: 99%

See 1 more Smart Citation

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

Kamarudin,

Suhaimi,

Nor Rashid

et al. 2024

Symmetry

0

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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.

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“…These devices are vulnerable to spoofing or impersonation attacks, which can be mitigated through the use of 2FA. Several studies have demonstrated the effectiveness of using 2FA to secure IoT systems from these types of attacks [1]- [6]. For example, [7] presents a 2FA protocol for IoT devices that utilizes physically unclonable functions and the characteristics of the wireless signal emitted by the device to secure it against spoofing and other types of attacks.…”

Section: Related Workmentioning

confidence: 99%

Wi-Fi Radio Wave-Based Continuous Zero-Effort Two-Factor Authentication Utilizes Machine Learning

AlQahtani¹,

Alshayeb²

2023

Preprint

0

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<p>Two-factor authentication (2FA) is a widely adopted security measure that requires users to provide an additional layer of authentication beyond a username and password in order to access protected resources. While traditional 2FA methods rely on the user to manually enter a code or token, the use of machine learning (ML) has the potential to revolutionize 2FA by enabling automatic and continuous verification of a user’s identity. In this paper, we propose a continuous zero effort 2FA system that uses ML to verify a user’s identity based on the unique characteristics of their environment, including beacon frame characteristics and RSSI values collected from Wi-Fi access points (APs). This system allows for seamless and automatic authentication without requiring any additional input or action from the user beyond their initial login. Additionally, the continuous authentication feature of the proposed system ensures that access to protected resources is maintained only when the user’s two devices are co-located. Through experiments, we demonstrate the effectiveness of the proposed system in determining the location of the user’s devices based on beacon frame characteristics and Received Signal Strength Indicator (RSSI) values. The proposed system offers a convenient and secure solution for 2FA that utilizes the power of ML to enable automatic and continuous authentication. Furthermore, its scalability, flexibility, and adjustability make it a promising option for organizations and users who need a secure and convenient authentication system. Finally, we have included three Python codes in the appendix, which provide further insights into our evaluation and analysis of the proposed system.</p>

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“…((name, feature_importance)) 46 47 # create table of results 48print(" +------------------+-------------------------+" ) 49 print("| Model Name | Feature Importance |") 50 print(" +------------------+-------------------------+" )51 for result in results: 52 print("| " + result[0].ljust(16) + " " + 53 "| " + str(result[1]). ljust(30) + " " + 54 "|")55 print(" +------------------+-------------------------+" ) .sum(feature_importance) 64…”

mentioning

confidence: 99%

Wi-Fi Radio Wave-Based Continuous Zero-Effort Two-Factor Authentication Utilizes Machine Learning

Alqahtani¹,

Alshyab²

2023

Preprint

1

0

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<p>Two-factor authentication (2FA) is a widely adopted security measure that requires users to provide an additional layer of authentication beyond a username and password in order to access protected resources. While traditional 2FA methods rely on the user to manually enter a code or token, the use of machine learning (ML) has the potential to revolutionize 2FA by enabling automatic and continuous verification of a user’s identity. In this paper, we propose a continuous zero effort 2FA system that uses ML to verify a user’s identity based on the unique characteristics of their environment, including beacon frame characteristics and RSSI values collected from Wi-Fi access points (APs). This system allows for seamless and automatic authentication without requiring any additional input or action from the user beyond their initial login. Additionally, the continuous authentication feature of the proposed system ensures that access to protected resources is maintained only when the user’s two devices are co-located. Through experiments, we demonstrate the effectiveness of the proposed system in determining the location of the user’s devices based on beacon frame characteristics and Received Signal Strength Indicator (RSSI) values. The proposed system offers a convenient and secure solution for 2FA that utilizes the power of ML to enable automatic and continuous authentication. Furthermore, its scalability, flexibility, and adjustability make it a promising option for organizations and users who need a secure and convenient authentication system. Finally, we have included three Python codes in the appendix, which provide further insights into our evaluation and analysis of the proposed system.</p>

show abstract

Secure Two-Factor Authentication for IoT Device

Cited by 9 publications

References 36 publications

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

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

Wi-Fi Radio Wave-Based Continuous Zero-Effort Two-Factor Authentication Utilizes Machine Learning

Wi-Fi Radio Wave-Based Continuous Zero-Effort Two-Factor Authentication Utilizes Machine Learning

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