Authentication Framework Research Articles

EEG Identity Authentication in Multi-Domain Features: A Multi-Scale 3D-CNN Approach.

Electroencephalogram (EEG) authentication has become a research hotspot in the field of information security due to its advantages of living, internal, and anti-stress. However, the performance of identity authentication system is limited by the inherent attributes of EEG, such as low SNR, low stability, and strong randomness. Researchers generally believe that the in-depth fusion of features can improve the performance of identity authentication and have explored among various feature domains. This experiment invited 70 subjects to participate in the EEG identity authentication task, and the experimental materials were visual stimuli of the self and non-self-names. This paper proposes an innovative EEG authentication framework, including efficient three-dimensional representation of EEG signals, multi-scale convolution structure, and the combination of multiple authentication strategies. In this work, individual EEG signals are converted into spatial–temporal–frequency domain three-dimensional forms to provide multi-angle mixed feature representation. Then, the individual identity features are extracted by the various convolution kernel of multi-scale vision, and the strategy of combining multiple convolution kernels is explored. The results show that the small-size and long-shape convolution kernel is suitable for ERP tasks, which can obtain better convergence and accuracy. The experimental results show that the classification performance of the proposed framework is excellent, and the multi-scale convolution method is effective to extract high-quality identity characteristics across feature domains. The results show that the branch number matches the EEG component number can obtain the excellent cost performance. In addition, this paper explores the network training performance for multi-scale module combination strategy and provides reference for deep network construction strategy of EEG signal processing.

How does engaging in authentic research at undergraduate level contribute to student well-being?

ABSTRACT In the context of rapidly growing numbers of university students reporting that they have experienced mental health problems, this paper argues that doing research as an undergraduate can contribute to student well-being. Although the benefits of undergraduate research are well documented, underlying reasons for its efficacy on positive student outcomes require investigation. This paper uses Self-Determination theory, which has empirically shown that fulfilling one’s need for autonomy, competence, and relatedness fosters well-being. Using authenticity as a conceptual lens to understand undergraduate research experiences, and a novel connection to Self-Determination theory, the theoretical proposition that authenticity within undergraduate research contributes to well-being is elaborated. The paper suggests that authenticity within undergraduate research experiences offers a way to stimulate well-being among our students, which hinges on effective curriculum design and mentoring. Two case studies, drawn from Medicine and Geography, explore the way in which curriculum design coupled with mentoring pedagogy can enhance authenticity in research, student motivation, and, therefore, well-being. The paper reveals how authentic research-based learning can form an entitlement for all students through an embedded curriculum-based approach. The paper advocates for research-based learning to begin early in the undergraduate curriculum, in order to establish a sense of belonging for students and healthy learner-centred pedagogy. In addition to the framework for authenticity adopted, this paper reveals the importance of attention to the quality of learning spaces (novelty, professionalism, inclusivity) and the practice-based elements of mentoring for effective relationships between learners and teachers to ensure the well-being of all involved.

Game Theoretic Physical Layer Authentication for Spoofing Detection in UAV Communications

In this paper, we propose a game theoretic framework for secure physical layer authentication (PLA) by applying the spatial correlations of received signal strength (RSS) in the air-to-ground channels between an unmanned aerial vehicle (UAV), a legitimate transmitter, and a spoofer. Based on our hypothesis model, we first derive the false alarm rate and miss detection rate for the UAV receiver. We then formulate a zero-sum PLA game where the UAV receiver selects its RSS detection threshold while the spoofer selects its attack probability to maximize their respective utilities. We derive the Nash equilibrium (NE) and its existence condition for the proposed PLA game. Monte Carlo simulation results accurately verify our analytical expressions for the false alarm rate and miss detection rate. Numerical results highlight the complex interplay between the detection threshold, spoofing attack probability and UAV altitude in the derived NE.

A signature-based data security and authentication framework for internet of things applications

<span>Internet of things (IoT) is the next big revolution in modernized network technologies connecting a massive number of heterogeneous smart appliances and physical objects. Owing to these technologies' novelty, various issues are characterized by security concerns are the most prioritized issue. A review of existing security approaches highlights that they are very particular about the solution towards a specific attack and cannot resist any unknown attacker. Therefore, this manuscript presents a novel computational model that introduces a unique authentication process using a simplified encryption strategy. The simulated study outcome shows that the proposed system offers efficient security and efficient data transmission performance in the presence of an unknown adversary. Hence, the study outcome exhibits better effects than frequently used security solutions when implemented in a vulnerable IoT environment.</span>

A framework of dynamic selection method for user classification in touch-based continuous mobile device authentication

Continuous authentication can provide a mechanism to continuously monitor mobile devices while a user is actively using it, after passing the initial-login authentication phase. Touch biometric is one of the promising modality to realise continuous authentication on mobile devices by distinguishing between the touch strokes performed by the legitimate and illegitimate users through classification algorithms. While the benefit of the scheme is promising, the effectiveness of different classification methods are not thoroughly understood. Little consideration has been given on the combination of multiple classifiers to perform continuous authentication. In this paper, we propose a novel classification framework for touch-based continuous mobile device authentication (CMDA), utilising dynamic selection of classifiers (DS). Instead of classifying all touch strokes using the same classifier, the proposed framework classifies each touch sample using the most promising classifier(s) from a pool of classifiers. Based on the proposed framework, we evaluated various DS methods in multiple scenarios across four touch datasets. The aim of this evaluation is to assess the feasibility of DS on touch-based CMDA. We then compared these DS methods with well-known single classifiers and static ensemble methods. The experimental results show the potential and feasibility of the DS methods to improve the authentication performance of touch-based CMDA against the benchmark methods. We found that DS methods are capable of producing promising results with relatively low equal error rate (EER) in many scenarios of the datasets, with relatively high consistencies. The obtained results would be valuable for further enhancement of existing user classification methods and the development of new DS methods in touch-based CMDA.

A Sociotechnical Framework for Semantic Biomedical Content Authoring and Publishing

Due to the ubiquity of unstructured biomedical data, significant obstacles still remain in achieving accurate and fast access to online biomedical content. In lieu of the growing volume of biomedical content on the web, embedding semantic annotations has become key to enhancing search engine context-aware indexing, thereby improving search speeds and retrieval accuracy. We introduce Semantically: a socio-technical framework for semantic biomedical content authoring and publishing. Identifying the appropriate semantic vocabulary for biomedical content annotation is a time-consuming and technically challenging process. Semantically automates this search by recommending highly accurate annotations from a wide range of biomedical ontologies. Furthermore, the framework is integrated with a knowledge-sharing system which allows biomedical authors to collaborate on identifying precise annotations during the content authoring process. Similarly, preserving content-level semantics during and after publishing to foster semantic search remains a research challenge. Semantically addresses this barrier by extending Schema.org, a community-agreed and research engine endorsed guideline for publishing structured content on the web. gosemantically.com

Seamless privacy-preservation and authentication framework for IoT-enabled smart eHealth systems

The initiatives of sustainable urbanization use ICT-based innovation to improve service efficiencies of smart cities and to discover modern economic opportunities. The smart sustainable cities prefer a reliable IoT connectivity and data integration to meet the standard constraints of emerging computing paradigms with enabling techhnologies. The computing paradigms including cloud, fog, and edge are nowadays driving the technological advancement to enrich the features of the digital application (DApp) including interactive, intuitive, and personalized. DApp utilizes the essential characteristics of the enabling technologies such as heterogeneity, resource constraints, connectivity, and mobility to standardize the nature of smart intelligence via service agents. In the connection of massive IoT services, DApp involves continuous generation of IoT data over a dedicated gateway to offer extensive connectivity and improved access control. In the past, most of the existing mechanisms have primarily focused on security management for IoT networks to operate massive application services. However, a key issue of privacy-preserving for massive IoT is still challenging to ensure device protection. Importantly, the existing mechanisms fail to claim security efficiencies in terms of user anonymity and authentication key exchange using formal proof. Thus, this paper presents a seamless authentication framework with privacy-preserving (SAF-PP) protocol to deal with security and privacy issues of smart eHealth intelligence. The formal analysis proves that the proposed SAF-PP can adhere to significant security properties while improving the system efficiency rate. The performance analysis also shows that the proposed SAF-PP is more efficient in increasing the active users with an improved packet delivery ratio and network lifetime.

Authoring Virtual Crowds: A Survey

AbstractRecent advancements in crowd simulation unravel a wide range of functionalities for virtual agents, delivering highly‐realistic, natural virtual crowds. Such systems are of particular importance to a variety of applications in fields such as: entertainment (e.g., movies, computer games); architectural and urban planning; and simulations for sports and training. However, providing their capabilities to untrained users necessitates the development of authoring frameworks. Authoring virtual crowds is a complex and multi‐level task, varying from assuming control and assisting users to realise their creative intents, to delivering intuitive and easy to use interfaces, facilitating such control. In this paper, we present a categorisation of the authorable crowd simulation components, ranging from high‐level behaviours and path‐planning to local movements, as well as animation and visualisation. We provide a review of the most relevant methods in each area, emphasising the amount and nature of influence that the users have over the final result. Moreover, we discuss the currently available authoring tools (e.g., graphical user interfaces, drag‐and‐drop), identifying the trends of early and recent work. Finally, we suggest promising directions for future research that mainly stem from the rise of learning‐based methods, and the need for a unified authoring framework.

CyberChain: Cybertwin Empowered Blockchain for Lightweight and Privacy-Preserving Authentication in Internet of Vehicles

Internet of Vehicles (IoVs) presents promising opportunities for vehicle to everything (V2X) applications, wherein authentication acts as the cornerstone to realize trustworthy vehicular context and to support advanced applications. However, existing authentication schemes mainly depend on centralized servers with both security and privacy issues. In this paper, we propose a CyberTwin (CT) empowered blockchain framework for authentication, namely CyberChain, to reduce both the communication and storage cost while maintaining vehicular privacy. By designing a blockchain system in the cyberspace, we decouple the consensus process from the physical world, so that the operation cost of blockchain can be reduced. A Privacy-Preserving Parallel Pedersen Commitment (P4C) algorithm is designed to protect the privacy of vehicles and accelerate the authentication process. To further enhance the operation efficiency of CyberChain, we propose a Diffused Practical Byzantine Fault Tolerance (DPBFT) mechanism to reach consensus in the cyberspace that can reduce consensus latency. The proposed cyberchain framework and the associated mechanisms are evaluated by qualitative analysis and simulations. The evaluation results demonstrated that the proposed cyberchain based framework significantly improves the authentication performance in terms of authentication latency, privacy, communication overhead and storage cost.

A Distributed Authentication Scheme Based on Smart Contract for Roaming Service in Mobile Vehicular Networks

Secure and real-time communication is an essential condition in mobile vehicular networks, and this requires secure authentication and seamless access enabled by roaming services. As a security inspector, roaming authentication ensures that legitimate users can access the network securely. However, today’s roaming authentication protocols authenticate users with the help of centralized authentication servers, leading to the risk of the single point of failure and roaming fraud. The massive device access in 5G networks further exacerbates the losses when problems occur. In light of it, we propose a decentralized fraud-proof roaming authentication framework based on blockchain. We leverage smart contracts to implement a roaming authentication protocol, including user/AP registration, authentication, and revocation. For higher efficiency, we utilize the Bloom filter for the revocation process. In addition, we design an unforgeable and undeniable billing scheme based on hash chain technology. Security and performance analysis show that the proposed roaming authentication scheme can provide the required security features while incurring an acceptable authentication delay.

CP-BDHCA: Blockchain-Based Confidentiality-Privacy Preserving Big Data Scheme for Healthcare Clouds and Applications.

Healthcare big data (HBD) allows medical stakeholders to analyze, access, retrieve personal and electronic health records (EHR) of patients. Mostly, the records are stored on healthcare cloud and application (HCA) servers, and thus, are subjected to end-user latency, extensive computations, single-point failures, and security and privacy risks. A joint solution is required to address the issues of responsive analytics, coupled with high data ingestion in HBD and secure EHR access. Motivated from the research gaps, the paper proposes a scheme, that integrates blockchain (BC)-based confidentiality-privacy (CP) preserving scheme, CP-BDHCA, that operates in two phases. In the first phase, elliptic curve cryptographic (ECC)-based digital signature framework, HCA-ECC is proposed to establish a session key for secure communication among different healthcare entities. Then, in the second phase, a two-step authentication framework is proposed that integrates Rivest-Shamir-Adleman (RSA) and advanced encryption standard (AES), named as HCA-RSAE that safeguards the ecosystem against possible attack vectors. CP-BDAHCA is compared against existing HCA cloud applications in terms of parameters like response time, average delay, transaction and signing costs, signing and verifying of mined blocks, and resistance to DoS and DDoS attacks. We consider 10 BC nodes and create a real-world customized dataset to be used with SEER dataset. The dataset has 30,000 patient profiles, with 1000 clinical accounts. Based on the combined dataset the proposed scheme outperforms traditional schemes like AI4SAFE, TEE, Secret, and IIoTEED, with a lower response time. For example, the scheme has a very less response time of 300 ms in DDoS. The average signing cost of mined BC transactions is 3,34 seconds, and for 205 transactions, has a signing delay of 1405 ms, with improved accuracy of ≈ 12% than conventional state-of-the-art approaches.

A Survey on Hadoop Security and Comparative Analysis on Authentication Frameworks in Hadoop Clusters

This paper presents a survey on various security frameworks which aim to secure Hadoop clusters and the files stored there. Most of the Hadoop platforms rely on Kerberos Authentication Protocol for user authentication. It is known that Kerberos itself has some vulnerabilities like Password Guessing Attacks, Single Point of Failure, Insider Attacks, and Time Synchronization problems. A new authentication framework that can address most of the identified issues and is computationally feasible than other schemes is introduced in this paper. The proposed framework is based on Secure Remote Password Protocol (SRP), Threshold Cryptography, and Blockchain Technology. The system focus on eradicating Password Guessing Attacks and Single Point of Failure in Kerberos Enabled Hadoop Clusters. The analysis results strengthen the claim that the proposed scheme beats the others in terms of computational efficiency and feasibility while offering the same security features. The paper is concluded with directions for future work.

RAFI: Robust Authentication Framework for IoT-Based RFID Infrastructure.

The Internet of Things (IoT) is a future trend that uses the Internet to connect a variety of physical things with the cyber world. IoT technology is rapidly evolving, and it will soon have a significant impact on our daily lives. While the growing number of linked IoT devices makes our daily lives easier, it also puts our personal data at risk. In IoT applications, Radio Frequency Identification (RFID) helps in the automatic identification of linked devices, and the dataflow of the system forms a symmetry in communication between the tags and the readers. However, the security and privacy of RFID-tag-connected devices are the key concerns. The communication link is thought to be wireless or insecure, making the RFID system open to several known threats. In order to address these security issues, we propose a robust authentication framework for IoT-based RFID infrastructure. We use formal security analysis in the random oracle model, as well as information analysis to support the claim of secure communication. Regarding the desirable performance characteristics, we describe and analyze the proposed framework’s performance and compare it to similar systems. According to our findings, the proposed framework satisfies all security requirements while also improving the communication.

Design of Lightweight Authentication Protocol for Fog enabled Internet of Things- A Centralized Authentication Framework

Internet is a large network of networks that spans the entire globe. Internet is playing indispensable role in our daily lives. The physical things are connected to internet with the help of digital identity. With recent advancement of information and communication technologies IoT became vital part of human life. However, IoT is not having standardized architecture. Nowadays IoT is integrated with fog computing which extends platform of cloud computing by providing computing resources on edges of computer network. Fog computing is motivated by IOT and It is decentralized solution for IoT. In addition, Fog computing has supported features like geographic distribution, low latency, location awareness, operate on premise, installed on heterogeneous hardware. IoT with cloud computing does not have such features. Therefore, in this paper, at first we discuss about the distributed fog computing architecture. Subsequently, we address the problem of authentication and design a new authentication framework for fog enabled IOT environment. It is stated that the proposed authentication framework will be useful in many IoT applications such as healthcare system, transportation system, smart cities, home energy management etc.

Mutual authentication framework using fog computing in healthcare

Mutual authentication framework using fog computing in healthcare

KMFA2 based QoS improvement for multi‐channel IoT networks

AbstractInternet of Things (IoT) networks are designed to obtain the best possible end‐user experience. This experience is measured in terms of security and speed of operation. In order to achieve high security, encryption, and authentication algorithms are proposed by researchers. The speed of operation is generally measured using the network's response time, and it can be improved using light‐weighted algorithms for data processing and actuation. Most of these techniques are applied at the application level, making the network ineffective in its originally deployed form. Moreover, due to the addition of security and data processing layers, the overall IoT network's QoS reduces, reducing the end user's experience. In order to remove these issues, this work proposes a novel kernel‐level multi‐channel fuzzy‐authentication and aggregation framework (KMFA2). The KMFA2 framework can solve authentication issues by using a kernel‐level lightweight and secure authentication protocol for all IoT devices. KMFA2 also enables the IoT devices to possess multi‐channel capabilities, with inbuilt aggregation capabilities. The overall implementation is carried out on the NS2 tool. The proposed KMFA2 protocol is tested on different IoT networks, and it is observed that the protocol improves the QoS by almost 10% and simplifies security using the authentication framework. The comparisons were performed with state‐of‐the‐art IoT deployments.

A Novel Framework for Open-Set Authentication of Internet of Things Using Limited Devices.

The Internet of Things (IoT) is promising to transform a wide range of fields. However, the open nature of IoT makes it exposed to cybersecurity threats, among which identity spoofing is a typical example. Physical layer authentication, which identifies IoT devices based on the physical layer characteristics of signals, serves as an effective way to counteract identity spoofing. In this paper, we propose a deep learning-based framework for the open-set authentication of IoT devices. Specifically, additive angular margin softmax (AAMSoftmax) was utilized to enhance the discriminability of learned features and a modified OpenMAX classifier was employed to adaptively identify authorized devices and distinguish unauthorized ones. The experimental results for both simulated data and real ADS–B (Automatic Dependent Surveillance–Broadcast) data indicate that our framework achieved superior performance compared to current approaches, especially when the number of devices used for training is limited.

Unconditional Authentication Based on Physical Layer Offered Chain Key in Wireless Communication.

Authentication is a critical issue in wireless communication due to the impersonation and substitution attacks from the vulnerable air interface launched by the malicious node. There are currently two kinds of authentication research in wireless communication. One is based on cryptography and relies on computational complexity, the other is based on physical layer fingerprint and can not protect data integrity well. Both of these approaches will become insecure when facing attackers with infinite computing power. In this paper, we develop a wireless unconditional authentication framework based on one-time keys generated from wireless channel. The proposed unconditional authentication framework provides a new perspective to resist infinite computing power attackers. We study the performance of the unconditional authentication framework in this paper. First, a physical layer offered chain key (PHYLOCK) structure is proposed, which can provide one-time keys for unconditional authentication. The physical layer offered chain keys are generated by XORing the physical layer updated keys extracted from the current channel state information (CSI) and the previous chain keys. The security of PHYLOCK is analyzed from the perspective of information theory. Then, the boundary of the deception probability is conducted. It is shown that unconditional authentication can achieve a probability of deception , where is the entropy of the one-time key used for one message. Finally, the conditions for unconditional authentication are listed. Our analysis shows that the length of the key and the authentication code need to be twice the length of the message and the encoding rules of the authentication code need to satisfy the restrictions we listed.

GTM approach towards engineering a features-oriented evaluation framework for secure authentication in IIoT environment

Industry Internet of Things (IIoT) has become the most evolving area over the last few years. The number of IoT devices connected in industry has sharply elevated but this surge has led to the vulnerability and data breach such as if a malicious entry is made to the secure network, it will forfeit all the network resources. For this purpose a full pledged secure authentication method is essential to safeguard IIoT network. There is a bulky list of number of authentications protocols available to keep network safe with a variety of features so but it becomes herculean task for network administrator to pick the strong and secure authentication method due to huge number of criteria, conflicting objectives and availability of authentication protocols in industry environment. It has become imperative to get the most rational authentication method in devices operating in IIoT. To address this issue, a feature-oriented assessment framework is put forward to provide a ground for ranking and selection of best authentication mechanism. This framework uses a mathematic approach known as Graph Theory Matrix Approach (GTMA) and selects the best authentication method based on the number of features. These features are related to authentication and covers almost every aspect of authentication method and are used as benchmark for selection purposes. This framework takes into account the most important features and helps in selecting the best and most ideal features-oriented authentication method that can be employed in IIoT to keep the integrity and security of connected devices and overall network infrastructure.

A Deep Learning Framework for Image Authentication: An Automatic Source Camera Identification Deep-Net

A Deep Learning Framework for Image Authentication: An Automatic Source Camera Identification Deep-Net