Realistic Authentication Research Articles

Decentralized Identity Authentication with Auditability and Privacy

Decentralized identity (DID) systems aim to give users full control over their identities by using completely decentralized technologies, such as blockchain or distributed ledgers, as identity providers. However, when user credentials are compromised, it is impossible in existing DID systems for the users to detect credential misuse. In this paper, we propose new DID authentication protocols with two properties: auditability and privacy. The former enables the detection of malicious authentication events, while the latter prevents an adversary from linking an authentication event to the corresponding user and service provider. We present two protocols that achieve auditability with varying privacy and performance guarantees. The first protocol has high performance, but it reveals information about the user. The second protocol achieves full privacy, but it incurs a higher performance overhead. We present a formal security analysis of our privacy-preserving protocols by using the Tamarin prover. We implemented them and evaluated their performance with a permissioned blockchain deployed over the Amazon AWS and a local cloud infrastructure. The results demonstrate that the first protocol is able to support realistic authentication workloads, while the second is nearly practical.

Ear Biometrics and Face Mask Detection Using Haar Features

The world depends on systems to provide secure environments and services to the people. Biometrics authentication (or realistic authentication) is employed in applied science as a style of identification and access control. Biometric System for authentic identification of a private. My motto is to make a biometric system using the ear as the main object. It also has no changes as expression change The human ear are neither affected by expressions like faces are nor do need closer touching like finger-prints do, which is more useful in the situation where the protection mechanism is needed as like in the pandemic Covid19 situation. Ear biometrics appears to be an accurate approach to an ever increasing demand for security in the common spaces. Then the robust feature extraction method is often accustomed determine personality of some individuals, as an example terrorist at the airport terminals. Ear as biometrics is often used in multimodal systems to improve the performance of some other known biometrics. In this paper, a novel algorithm was proposed to do face mask detection and ear recognition using deep convolutional neural network and provide a visualization of the learned network. Also a temperature sensor is included. Only after the checking of facemask the subject is allowed to ear biometrics for more security reason.

A practical and communication-efficient deniable authentication with source-hiding and its application on Wi-Fi privacy

Authentication service in communication is essential. However, information flows transmitted during an authentication process might reveal some personal information. In this paper, we propose a deniable authentication protocol with source hiding which does not reveal any private information, therefore the privacy of participants is preserved. With our approach, the sender is able to deny an authentication process to any third party. The receiver can not prove that the sender has participated in this authentication, though it is sure that the sender is legitimate. We construct this protocol without using “Encryption-then-MAC” paradigm, therefore the underlying building block is not required to be CCA2 secure, and is a more realistic authentication protocol for practical privacy-preserving Internet-based applications. We also show how to apply it in Wi-Fi authentication to prevent the location leakage.

Reduced Optimal Feature Based Biometric Authentication Using MALO-MKSVM Techniques

Biometric authentication is referred to as a realistic authentication which traits used is distinct, and quantifiable to recognize one individual. Depending on the level of security required, unimodal based authentication mechanisms are prone to numerous security attacks. In this paper, we propose a multimodal based biometric recognition framework which will improve the security level by using more than one type of biometric scanner. A new multimodal feature extraction technique has been proposed to reduce the features by utilizing Probabilistic Principal Component Analysis (PPCA) model by the way of choosing optimal features with the assistance of Modified Ant Lion Optimization (MALO). Finally, the recognized and non-recognized images are accomplished by the formation of a new classification model i.e. Multi Kernel Support Vector Machine (MKSVM). From this procedure, the result showed that a high recognition rate and also the most extreme accuracy accomplished in this work.

A Realistic Lightweight Anonymous Authentication Protocol for Securing Real-Time Application Data Access in Wireless Sensor Networks

User authentication in wireless sensor networks (WSN) is a critical security issue due to their unattended and hostile deployment in the field. Since the sensor nodes are equipped with limited computing power, storage, and communication modules, authenticating remote users in such resource-constrained environment is a paramount security concern. Until now, impressive efforts have been made for designing authentication schemes with user anonymity by using only the lightweight cryptographic primitives, such as symmetric key encryption/decryption and hash functions. However, to the best of our knowledge, none has succeeded so far. In this paper, we take an initial step to shed light on the rationale underlying this prominent issue. In order to do that here at first, we demonstrate that the existing solutions for anonymous user authentication in WSN are impractical. Subsequently, we propose a realistic authentication protocol for WSN, which can ensure various imperative security properties like user anonymity, untraceability, forward/backward secrecy, perfect forward secrecy, etc.

An EAP-EHash authentication method adapted to resource constrained terminals

In the era of mobile and wireless networks, the growing complexity of end devices and the accentuated tendency towards miniaturization of them raise new security challenges. Authentication is a crucial concern in resource constrained environments, and despite the great number of existing EAP methods, as explained in the article, we are still in need for EAP methods tightly adapted to wireless environments and satisfying heterogeneity of terminals and their limitations of resources. After a first comparative analysis of existing EAP methods, this article presents a new EAP-EHash method (EHash for encrypted hash) that is adapted to the highly vulnerable wireless environment by supporting mutual authentication and session key derivation and offering simplicity, rapidity, and easy-to-deploy features. This EAP-EHash was formally proven to satisfy the claimed security properties, thanks to the AVISPA tool. Implementation of it on an 802.11 testbed platform gave realistic authentication delays averaging 26 ms and thus proved that EAP-EHash is competitive to EAP-MD5 that is known to be the simplest of the EAP methods. Features of EAP-EHash include short execution delays and low bandwidth consumption, and as such, it appears attractive for wireless.

Acoustic emission technology for detection of shaft-to-seal rubbing on power generation turbines; a qualitative verification

This brief report presents the most realistic authentication of the applicability of Acoustic Emission (AE) technology for the detection of rotor-to-stator rubbing in power generation steam turbines. Rubbing was induced on an operational 200 MW steam turbine by pressing a brass-tipped rubbing stick against an exposed region of the shaft. AE and vibration measurements were recorded throughout the tests and it was noted that AE provided an immediate response to the application of the rubbing stick.