Search Results (105)

Nowadays, user-generated content has become a crucial channel for obtaining information and authentic feedback. However, due to the varying cultural and educational levels of online users, the content of online reviews often suffers from inconsistencies in specification and the inclusion of arbitrary information. Consequently, the task of extracting key information from online reviews has become a prominent area of research. This paper proposes a combined entity recognition model for online reviews, aiming to improve the accuracy of Named Entity Recognition (NER). Initially, the Non-negative Matrix Factorization (NMF) model is employed to perform thematic clustering on the review texts, and entity types are extracted based on the clustering results. Subsequently, we introduce an entity recognition model utilizing the pre-trained BERT model as an embedding layer, with BiLSTM and DGCNN incorporating residual connection and gating mechanisms as feature extraction layers. The model also leverages multi-head attention for feature fusion, and the final results are decoded using a Conditional Random Field (CRF) layer. The model achieves an F1 score of 86.8383% on a collected dataset of online reviews containing eight entity categories. Experimental results demonstrate that the proposed model outperforms other mainstream NER models, effectively identifying key entities in online reviews. Full article

The Zero Trust Architecture (ZTA) security system follows the “never trust, always verify” principle. The process constantly verifies users and devices trying to access resources. This paper describes how Microsoft Azure uses ZTA to enforce strict identity verification and access rules across the cloud environment to improve security. Implementation takes time and effort. Azure’s extensive services and customizations require careful design and implementation. Azure administrators need help navigating and changing configurations due to its complex user interface (UI). Each Azure ecosystem component must meet ZTA criteria. ZTAs comprehensive policy definitions, multi-factor and passwordless authentication, and other advanced features are tested in a mid-size business scenario. The document delineates several principal findings concerning the execution of Azure’s ZTA within mid-sized enterprises. Azure ZTA significantly improves security by reducing attack surfaces via ongoing identity verification, stringent access controls, and micro-segmentation. Nonetheless, its execution is resource-demanding and intricate, necessitating considerable expertise and meticulous planning. A notable disparity exists between theoretical ZTA frameworks and their practical implementation, characterized by disjointed management interfaces and user fatigue resulting from incessant authentication requests. The case studies indicate that although Zero Trust Architecture enhances organizational security and mitigates risks, it may disrupt operations and adversely affect user experience, particularly in hybrid and fully cloud-based settings. The study underscores the necessity for customized configurations and the equilibrium between security and usability to ensure effective ZTA implementation. Full article

Extended Reality (XR), encompassing Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), enables immersive experiences across various fields, including entertainment, healthcare, and education. However, its data-intensive and interactive nature introduces significant cybersecurity and privacy challenges. This paper presents a detailed adversary model to identify threat actors and attack vectors in XR environments. We analyze key risks, including identity theft and behavioral data leakage, which can lead to profiling, manipulation, or invasive targeted advertising. To mitigate these risks, we explore technical solutions such as Advanced Encryption Standard (AES), Rivest–Shamir–Adleman (RSA), and Elliptic Curve Cryptography (ECC) for secure data transmission, multi-factor and biometric authentication, data anonymization techniques, and AI-driven anomaly detection for real-time threat monitoring. A comparative benchmark evaluates these solutions’ practicality, strengths, and limitations in XR applications. The findings emphasize the need for a holistic approach, combining robust technical measures with privacy-centric policies, to secure XR ecosystems and ensure user trust. Full article

The integration of artificial intelligence(AI) technologies into identity and access management (IAM) systems has greatly improved access control and management, offering more robust, adaptive, and intelligent solutions than traditional methods. AI-driven IAM systems enhance security, operational efficiency, and introduce new capabilities in industrial environments. In this narrative review, we present the state-of-the-art AI technologies in industrial IAM, focusing on methods such as biometric, comprising facial and voice recognition, and multifactor authentication for robust security. It addresses the challenges and solutions in implementing AI-based IAM systems in industrial settings, including security, privacy, evaluation, and continuous improvement. We present also the emerging trends and future directions, highlighting AI’s potential to transform industrial security measures. This review aims to guide researchers and practitioners in developing and implementing next-generation access control systems, proposing future research directions to address challenges and optimize AI applications in this domain. Full article

With recent significant advancements in artificial intelligence, the necessity for more reliable recognition systems has rapidly increased to safeguard individual assets. The use of brain signals for authentication has gained substantial interest within the scientific community over the past decade. Most previous efforts have focused on identifying distinctive information within electroencephalogram (EEG) recordings. In this study, an EEG-based user authentication scheme is presented, employing a multi-layer perceptron feedforward neural network (MLP FFNN). The scheme utilizes P300 potentials derived from EEG signals, focusing on the user’s intent to select specific characters. This approach involves two phases: user identification and user authentication. Both phases utilize EEG recordings of brain signals, data preprocessing, a database to store and manage these recordings for efficient retrieval and organization, and feature extraction using mutual information (MI) from selected EEG data segments, specifically targeting power spectral density (PSD) across five frequency bands. The user identification phase employs multi-class classifiers to predict the identity of a user from a set of enrolled users. The user authentication phase associates the predicted user identities with user labels using probability assessments, verifying the claimed identity as either genuine or an impostor. This scheme combines EEG data segments with user mapping, confidence calculations, and claimed user verification for robust authentication. It also accommodates new users by transforming EEG data into feature vectors without the need for retraining. The model extracts selected features to identify users and to classify the input based on these features to authenticate the user. The experiments show that the proposed scheme can achieve 97% accuracy in EEG-based user identification and authentication. Full article

In the proposed protocol, a trusted entity interacts with the terminal device of each user to verify the legitimacy of the public keys without having access to the private keys that are generated and kept totally secret by the user. The protocol introduces challenge–response–pair mechanisms enabling the generation, distribution, and verification of cryptographic public–private key pairs in a distributed network with multi-factor authentication, tokens, and template-less biometry. While protocols using generic digital signature algorithms are proposed, the focus of the experimental work was to implement a solution based on Crystals-Dilithium, a post-quantum cryptographic algorithm under standardization. Crystals-Dilithium generates public keys consisting of two interrelated parts, a matrix generating seed, and a vector computed from the matrix and two randomly picked vectors forming the secret key. We show how such a split of the public keys lends itself to a two-way authentication of both the trusted entity and the users. Full article

Passwords are the first line of defence against preventing unauthorised access to systems and potential leakage of sensitive data. However, the traditional reliance on username and password combinations is not enough protection and has prompted the implementation of technologies such as two-factor authentication (2FA). While 2FA enhances security by adding a layer of verification, these techniques are not impervious to threats. Even with the implementation of 2FA, the relentless efforts of cybercriminals present formidable obstacles in securing digital spaces. The objective of this work is to implement blockchain technology as a form of 2FA. The findings of this work suggest that blockchain-based 2FA methods could strengthen digital security compared to conventional 2FA methods. Full article

Remote data backup technology avoids the risk of data loss and tampering, and has higher security compared to local data backup solutions. However, the data transmission channel for remote data backup is not secure, and the backup server cannot be fully trusted, so users usually encrypt the data before uploading it to the remote server. As a result, how to protect this encryption key is crucial. We design a User-Centric Design (UCD) data backup scheme based on multi-factor authentication to protect this encryption key. Our scheme utilizes a secret sharing scheme to divide the encryption key into three parts, which are stored in the laptop, the smart card, and the server. The encryption key can be easily reconstructed from any two parts with user’s private information password, identity and biometrics. As long as the biometrics has enough entropy, our scheme can resist replay attacks, impersonation user attacks, impersonation server attacks, malicious servers and offline password guessing attacks. Full article

The overconsumption of clothing has detrimental impacts on society and the environment. For consumers, reducing consumption is complicated by the vital role that clothing plays in individual expression. This study examined the influence of personal values and clothing style confidence on consumers’ interest in upcycled clothing. An online Internet survey was used to gather data from a valid sample of 565 U.S. residents. Partial least squares structural equation modeling was used to analyze the data. Schwartz’s self-transcendence and self-enhancement values were modeled as antecedents to clothing style confidence (CSC), which is a multi-dimensional construct composed of five factors, including (1) style longevity, (2) aesthetic perceptive ability, (3) creativity, (4) appearance importance, and (5) authenticity. CSC was predicted to mediate the relationship between self-transcendence and self-enhancement values and interest in upcycled clothing, including the purchase of upcycled clothing and interest in learning how to upcycle clothing. Findings showed that CSC mediated the relationship between self-self-transcendence and self-enhancement values and interest in upcycled clothing, as predicted according to value–attitude–behavior theory. Results suggest that bolstering consumers’ confidence in personal style may provide intrinsic motivation for change, empowering individuals to embrace their personal style rather than follow fashion trends. Full article

Communication between connected objects in the Internet of Things (IoT) often requires secure and reliable authentication mechanisms to verify identities of entities and prevent unauthorized access to sensitive data and resources. Unlike other domains, IoT offers several advantages and opportunities, such as the ability to collect real-time data through numerous sensors. These data contains valuable information about the environment and other objects that, if used, can significantly enhance authentication processes. In this paper, we propose a novel idea to building opportunistic sensor-based authentication factors by leveraging existing IoT sensors in a system of systems approach. The objective is to highlight the promising prospects of opportunistic authentication factors in enhancing IoT security. We claim that sensors can be utilized to create additional authentication factors, thereby reinforcing existing object-to-object authentication mechanisms. By integrating these opportunistic sensor-based authentication factors into multi-factor authentication schemes, IoT security can be substantially improved. We demonstrate the feasibility and effectivenness of our idea through illustrative experiments in a parking entry scenario, involving both mobile robots and cars, achieving high identification accuracy. We highlight the potential of this novel method to improve IoT security and suggest future research directions for formalizing and comparing our approach with existing techniques. Full article

The fragmented remodeling of historic districts undermines the spirit of place. Understanding the intricate relationship between the neighborhood environment and the spirit of place is essential for sustainable urban development. Current research predominantly relies on case studies and the concept of place, which are subjective and lack specific analysis of how the neighborhood environment shapes the spirit of place. In this study, we examine Chuancheng Street in Handan City as a case study. Utilizing the eXtreme Gradient Boosting (XGBoost) model and multi-source data, combined with SHapley Additive exPlanation (SHAP) and Partial Dependence Plots (PDP), we analyze the non-linear and synergistic effects of the street environment on the spirit of place in historic districts. The findings reveal that (1) the proportion of enduring sociability (PES) on the street significantly shapes the spirit of place, with cultural space elements being less prioritized in perception; (2) PES, green vision index (GVI), Integration_800 m, and mixed land use (MLU) have pronounced non-linear impacts on the spirit of place, with strong threshold effects, and these factors also demonstrate a synergistic effect; (3) There are notable spatial variations in the spirit of place across different blocks, particularly influenced by the authenticity of cultural heritage. This study provides fundamental insights into the spirit of place in historic neighborhoods, enabling a better understanding of complex urban dynamics and informing future street regeneration from a place perspective. Full article

Multi-factor authentication (MFA) is a system for authenticating an individual’s identity using two or more pieces of data (known as factors). The reason for using more than two factors is to further strengthen security through the use of additional data for identity authentication. Sequential MFA requires a number of steps to be followed in sequence for authentication; for example, with three factors, the system requires three authentication steps. In this case, to proceed with MFA using a deep learning approach, three artificial neural networks (ANNs) are needed. In contrast, in parallel MFA, the authentication steps are processed simultaneously. This means that processing is possible with only one ANN. A convolutional neural network (CNN) is a method for learning images through the use of convolutional layers, and researchers have proposed several systems for MFA using CNNs in which various modalities have been employed, such as images, handwritten text for authentication, and multi-image data for machine learning of facial emotion. This study proposes a CNN-based parallel MFA system that uses concatenation. The three factors used for learning are a face image, an image converted from a password, and a specific image designated by the user. In addition, a secure password image is created at different bit-positions, enabling the user to securely hide their password information. Furthermore, users designate a specific image other than their face as an auxiliary image, which could be a photo of their pet dog or favorite fruit, or an image of one of their possessions, such as a car. In this way, authentication is rendered possible through learning the three factors—that is, the face, password, and specific auxiliary image—using the CNN. The contribution that this study makes to the existing body of knowledge is demonstrating that the development of an MFA system using a lightweight, mobile, multi-factor CNN (MMCNN), which can even be used in mobile devices due to its low number of parameters, is possible. Furthermore, an algorithm that can securely transform a text password into an image is proposed, and it is demonstrated that the three considered factors have the same weight of information for authentication based on the false acceptance rate (FAR) values experimentally obtained with the proposed system. Full article

Internet of Things (IoT) technology is evolving over the peak of smart infrastructure with the participation of IoT devices in a wide range of applications. Traditional IoT authentication methods are vulnerable to threats due to wireless data transmission. However, IoT devices are resource- and energy-constrained, so building lightweight security that provides stronger authentication is essential. This paper proposes a novel, two-layered multi-factor authentication (2L-MFA) framework using blockchain to enhance IoT devices and user security. The first level of authentication is for IoT devices, one that considers secret keys, geographical location, and physically unclonable function (PUF). Proof-of-authentication (PoAh) and elliptic curve Diffie–Hellman are followed for lightweight and low latency support. Second-level authentication for IoT users, which are sub-categorized into four levels, each defined by specific factors such as identity, password, and biometrics. The first level involves a matrix-based password; the second level utilizes the elliptic curve digital signature algorithm (ECDSA); and levels 3 and 4 are secured with iris and finger vein, providing comprehensive and robust authentication. We deployed fuzzy logic to validate the authentication and make the system more robust. The 2L-MFA model significantly improves performance, reducing registration, login, and authentication times by up to 25%, 50%, and 25%, respectively, facilitating quicker cloud access post-authentication and enhancing overall efficiency. Full article

As a type of financial derivative, the price fluctuation of futures is influenced by a multitude of factors, including macroeconomic conditions, policy changes, and market sentiment. The interaction of these factors makes the future trend become complex and difficult to predict. However, for investors, the ability to accurately predict the future trend of stock index futures price is directly related to the correctness of investment decisions and investment returns. Therefore, predicting the stock index futures market remains a leading and critical issue in the field of finance. To improve the accuracy of predicting stock index futures price, this paper introduces an innovative forecasting method by combining the strengths of Long Short-Term Memory (LSTM) networks and various Generalized Autoregressive Conditional Heteroskedasticity (GARCH)-family models namely, MULTI-GARCH-LSTM. This integrated approach is specifically designed to tackle the challenges posed by the nonstationary and nonlinear characteristics of stock index futures price series. This synergy not only enhances the model’s ability to capture a wide range of market behaviors but also significantly improves the precision of future price predictions, catering to the intricate nature of financial time series data. Initially, we extract insights into the volatility characteristics, such as the aggregation of volatility in futures closing prices, by formulating a model from the GARCH family. Subsequently, the LSTM model decodes the complex nonlinear relationships inherent in the futures price series and incorporates assimilated volatility characteristics to predict future prices. The efficacy of this model is validated by applying it to an authentic dataset of gold futures. The empirical findings demonstrate that the performance of our proposed MULTI-GARCH-LSTM hybrid model consistently surpasses that of the individual models, thereby confirming the model’s effectiveness and superior predictive capability. Full article

FIDO (Fast Identity Online) is a set of network identity standards established by the FIDO Alliance. It employs a framework based on public key cryptography to facilitate multi-factor authentication (MFA) and biometric login, ensuring the robust protection of personal data associated with cloud accounts and ensuring the security of server-to-terminal device protocols during the login process. The FIDO Alliance has established three standards: FIDO Universal Second Factor (FIDO U2F), FIDO Universal Authentication Framework (FIDO UAF), and the Client to Authenticator Protocols (CTAP). The newer CTAP, also known as FIDO2, integrates passwordless login and two-factor authentication. Importantly, FIDO2’s support for major browsers enables users to authenticate their identities via FIDO2 across a broader range of platforms and devices, ushering in the era of passwordless authentication. In the FIDO2 framework, if a user’s device is stolen or compromised, then the private key may be compromised, and the public key stored on the FIDO2 server may be tampered with by attackers attempting to impersonate the user for identity authentication, posing a high risk to information security. Recognizing this, this study aims to propose a solution based on the FIDO2 framework, combined with blockchain technology and access control, called the FIDO2 blockchain architecture, to address existing security vulnerabilities in FIDO2. By leveraging the decentralized nature of the blockchain, the study addresses potential single points of failure in FIDO2 server centralized identity management systems, thereby enhancing system security and availability. Furthermore, the immutability of the blockchain ensures the integrity of public keys once securely stored on the chain, effectively reducing the risk of attackers impersonating user identities. Additionally, the study implements an access control mechanism to manage user permissions effectively, ensuring that only authorized users can access corresponding permissions and preventing unauthorized modifications and abuse. In addition to proposing practical solutions and steps, the study explains and addresses security concerns and conducts performance evaluations. Overall, this study brings higher levels of security and trustworthiness to FIDO2, providing a robust identity authentication solution. Full article