L2AI: lightweight three-factor authentication and authorization in IOMT blockchain-based environment

• URL: http://arxiv.org/abs/2407.12187v1
• Date: Tue, 16 Jul 2024 21:33:46 GMT
• Title: L2AI: lightweight three-factor authentication and authorization in IOMT blockchain-based environment
• Authors: Laleh Khajehzadeh, Hamid Barati, Ali Barati,
• Abstract summary: Medical Internet of Things (IoMT) enables individuals to remotely manage their essential activities with minimal interaction. This paper presents a lightweight multi-factor authentication and anonymous user authentication scheme to access real-time data in a blockchain-based environment.
• Score: 0.6554326244334868
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Medical Internet of Things (IoMT) is the next frontier in the digital revolution and is utilized in healthcare. In this context, IoT enables individuals to remotely manage their essential activities with minimal interaction. However, the limitations of network resources and the challenges of establishing a secure channel, as well as sharing and collecting sensitive information through an insecure public channel, pose security challenges for the medical IoT. This paper presents a lightweight multi-factor authentication and anonymous user authentication scheme to access real-time data in a blockchain-based environment. The scheme utilizes an insecure channel called L2AI. L2AI ensures security and efficiency while enhancing user anonymity through the use of pseudo-identity and dynamic indexing. The proposed method supports highly scalable systems with an efficient user registration process, allowing authenticated users to access both existing and newly added system entities without additional processes. Although the scheme is primarily designed for large systems, such as health infrastructure, it is also suitable for resource-constrained devices. The scheme relies on one-way cryptographic hashing functions and bitwise XOR operations. Additionally, a fuzzy mining algorithm is employed on the user side to verify the user's biometric information. L2AI adopts the "Real-Or-Random (ROR)" model for security proof and employs BAN logic for proof of authenticity. Formal security verification is conducted using the "Automatic Validation of Internet Security Protocols and Programs" (Proverif) tool, complemented by informal security analysis demonstrating the proper functionality of L2AI.

Related papers

• 2FA: Navigating the Challenges and Solutions for Inclusive Access [55.2480439325792]
  Two-Factor Authentication (2FA) has emerged as a critical solution to protect online activities. This paper examines the intricacies of deploying 2FA in a way that is secure and accessible to all users. An analysis was conducted to examine the implementation and availability of various 2FA methods across popular online platforms.
  arXiv  Detail & Related papers  (2025-02-17T12:23:53Z)
• Cryptanalysis via Machine Learning Based Information Theoretic Metrics [58.96805474751668]
  We propose two novel applications of machine learning (ML) algorithms to perform cryptanalysis on any cryptosystem. These algorithms can be readily applied in an audit setting to evaluate the robustness of a cryptosystem. We show that our classification model correctly identifies the encryption schemes that are not IND-CPA secure, such as DES, RSA, and AES ECB, with high accuracy.
  arXiv  Detail & Related papers  (2025-01-25T04:53:36Z)
• Securing Legacy Communication Networks via Authenticated Cyclic Redundancy Integrity Check [98.34702864029796]
  We propose Authenticated Cyclic Redundancy Integrity Check (ACRIC) ACRIC preserves backward compatibility without requiring additional hardware and is protocol agnostic. We show that ACRIC offers robust security with minimal transmission overhead ( 1 ms)
  arXiv  Detail & Related papers  (2024-11-21T18:26:05Z)
• Leveraging Machine Learning for Wi-Fi-based Environmental Continuous Two-Factor Authentication [0.44998333629984877]
  We present a novel 2FA approach replacing the user's input with decisions made by Machine Learning (ML) Our system exploits unique environmental features associated with the user, such as beacon frame characteristics and Received Signal Strength Indicator ( RSSI) values from Wi-Fi Access Points (APs) For enhanced security, our system mandates that the user's two devices (i.e., a login device and a mobile device) be situated within a predetermined proximity before granting access.
  arXiv  Detail & Related papers  (2024-01-12T14:58:15Z)
• DynamiQS: Quantum Secure Authentication for Dynamic Charging of Electric Vehicles [61.394095512765304]
  Dynamic Wireless Power Transfer (DWPT) is a novel technology that allows charging an electric vehicle while driving. Recent advancements in quantum computing jeopardize classical public key cryptography. We propose DynamiQS, the first post-quantum secure authentication protocol for dynamic wireless charging.
  arXiv  Detail & Related papers  (2023-12-20T09:40:45Z)
• Blockchain-based Zero Trust on the Edge [5.323279718522213]
  This paper proposes a novel approach based on Zero Trust Architecture (ZTA) extended with blockchain to further enhance security. The blockchain component serves as an immutable database for storing users' requests and is used to verify trustworthiness by analyzing and identifying potentially malicious user activities. We discuss the framework, processes of the approach, and the experiments carried out on a testbed to validate its feasibility and applicability in the smart city context.
  arXiv  Detail & Related papers  (2023-11-28T12:43:21Z)
• A Lightweight and Secure PUF-Based Authentication and Key-exchange Protocol for IoT Devices [0.0]
  Device Authentication and Key exchange are major challenges for the Internet of Things. PUF appears to offer a practical and economical security mechanism in place of typically sophisticated cryptosystems like PKI and IBE. We present a system in which the IoT device does not require a continuous active internet connection to communicate with the server in order to Authenticate itself.
  arXiv  Detail & Related papers  (2023-11-07T15:42:14Z)
• When Authentication Is Not Enough: On the Security of Behavioral-Based Driver Authentication Systems [53.2306792009435]
  We develop two lightweight driver authentication systems based on Random Forest and Recurrent Neural Network architectures. We are the first to propose attacks against these systems by developing two novel evasion attacks, SMARTCAN and GANCAN. Through our contributions, we aid practitioners in safely adopting these systems, help reduce car thefts, and enhance driver security.
  arXiv  Detail & Related papers  (2023-06-09T14:33:26Z)
• Zero-Effort Two-Factor Authentication Using Wi-Fi Radio Wave Transmission and Machine Learning [0.0]
  This paper presents a novel zero-effort two-factor authentication (2FA) approach that combines the unique characteristics of a users environment and Machine Learning (ML) to confirm their identity. A prototype was developed using Raspberry Pi devices and experiments were conducted to demonstrate the effectiveness and practicality of the proposed approach. The proposed system holds great promise in revolutionizing the field of 2FA and user authentication, offering a new era of secure and seamless access to sensitive information.
  arXiv  Detail & Related papers  (2023-03-04T21:04:10Z)
• Safe RAN control: A Symbolic Reinforcement Learning Approach [62.997667081978825]
  We present a Symbolic Reinforcement Learning (SRL) based architecture for safety control of Radio Access Network (RAN) applications. We provide a purely automated procedure in which a user can specify high-level logical safety specifications for a given cellular network topology. We introduce a user interface (UI) developed to help a user set intent specifications to the system, and inspect the difference in agent proposed actions.
  arXiv  Detail & Related papers  (2021-06-03T16:45:40Z)
• Framework for a DLT Based COVID-19 Passport [0.0]
  We describe a mechanism that allows one to store the COVID-19 vaccination details of individuals on a publicly readable, decentralised, immutable blockchain. Our main contribution is the employment of a provably secure input-hiding, locality-sensitive hashing algorithm over an iris extraction technique.
  arXiv  Detail & Related papers  (2020-08-03T18:28:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.