Identity Authentication System Research Articles

AttackNet: Enhancing biometric security via tailored convolutional neural network architectures for liveness detection

Biometric security is the cornerstone of modern identity verification and authentication systems, where the integrity and reliability of biometric samples is of paramount importance. This paper introduces AttackNet, a bespoke Convolutional Neural Network architecture, meticulously designed to combat spoofing threats in biometric systems. Rooted in deep learning methodologies, this model offers a layered defense mechanism, seamlessly transitioning from low-level feature extraction to high-level pattern discernment. Three distinctive architectural phases form the crux of the model, each underpinned by judiciously chosen activation functions, normalization techniques, and dropout layers to ensure robustness and resilience against adversarial attacks. Benchmarking our model across diverse datasets affirms its prowess, showcasing superior performance metrics in comparison to contemporary models. Furthermore, a detailed comparative analysis accentuates the model's efficacy, drawing parallels with prevailing state-of-the-art methodologies. Through iterative refinement and an informed architectural strategy, AttackNet underscores the potential of deep learning in safeguarding the future of biometric security.

Versatile Machine Learning-Based Authentications by Using Enhanced Time-Sliced Electrocardiograms

This paper addresses the enhancement of modern security through the integration of electrocardiograms (ECGs) into biometric authentication systems. As technology advances, the demand for reliable identity authentication systems has grown, given the rise in breaches associated with traditional techniques that rely on unique biological and behavioral traits. These techniques are emerging as more reliable alternatives. Among the biological features used for authentication, ECGs offer unique advantages, including resistance to forgery, real-time detection, and continuous identification ability. A key contribution of this work is the introduction of a variant of the ECG time-slicing technique that outperforms existing ECG-based authentication methods. By leveraging machine learning algorithms and tailor-made compact data learning techniques, this research presents a more robust, reliable biometric authentication system. The findings could lead to significant advancements in network information security, with potential applications across various internet and mobile services.

INTEGRATING AI WITH BLOCKCHAIN FOR ENHANCED FINANCIAL SERVICES SECURITY

Integrating artificial intelligence (AI) with blockchain technology presents a transformative approach to enhancing security in financial services. This fusion leverages the strengths of both AI and blockchain to mitigate various security risks, including fraud, data breaches, and identity theft, thereby bolstering trust and confidence in financial transactions. This abstract explores the synergies between AI and blockchain, highlighting their combined capabilities, applications, and potential benefits for the financial services industry. AI algorithms, including machine learning and natural language processing, empower financial institutions to analyze vast amounts of data in real-time, identifying patterns, anomalies, and suspicious activities indicative of fraudulent behavior. By integrating AI-powered fraud detection systems with blockchain-based transactional networks, organizations can enhance security and transparency throughout the entire financial ecosystem. Blockchain's immutable ledger ensures the integrity and traceability of transactions, while AI algorithms provide advanced analytics and predictive insights to detect and prevent fraudulent activities effectively. Furthermore, AI-driven identity verification and authentication systems enhance security in digital transactions by accurately verifying user identities and detecting unauthorized access attempts. By integrating AI-based biometric authentication with blockchain-based identity management solutions, financial institutions can streamline customer onboarding processes, enhance security, and protect sensitive information from unauthorized access. Moreover, AI-powered smart contracts automate and enforce the execution of contractual agreements, reducing the risk of fraud, errors, and disputes in financial transactions. By combining AI-driven smart contract platforms with blockchain technology, organizations can facilitate secure, transparent, and tamper-proof transactions, eliminating intermediaries and reducing transaction costs. The integration of AI with blockchain also offers opportunities for regulatory compliance and risk management in financial services. AI-powered regulatory compliance solutions analyze vast amounts of regulatory data, identify compliance risks, and ensure adherence to regulatory requirements. By integrating these solutions with blockchain-based regulatory reporting systems, financial institutions can enhance transparency, auditability, and regulatory oversight, fostering trust and compliance in the financial ecosystem. In conclusion, the integration of AI with blockchain technology holds immense potential for enhancing security in financial services. By harnessing the combined capabilities of AI and blockchain, organizations can detect and prevent fraudulent activities, enhance identity verification and authentication, streamline transaction processes, and ensure regulatory compliance. This abstract underscores the transformative impact of integrating AI with blockchain on security in financial services, paving the way for a more secure, efficient, and trusted financial ecosystem..
 Keywords: Al, Blockchain, Enhanced, Financial, Services Security.

Research and Implementation of High Available Digital Campus Unified Identity Authentication System Based on LDAP

Research and Implementation of High Available Digital Campus Unified Identity Authentication System Based on LDAP

Research and Practice on High Availability Scheme of Unified Identity Authentication System Based on CAS in Colleges and Universities

Unified identity authentication has become the basic information service provided by colleges and universities for teachers and students. Security, stability, high concurrency and easy maintenance are our requirements for a unified identity authentication system. Based on the practical work experience of China University of Geosciences (Beijing), this paper proposes a high availability scheme of unified identity authentication system based on CAS, which is composed of multiple CAS Servers, Nginx for load balancing, and Redis as a cache database. The scheme has been practiced in China University of Geosciences (Beijing), and the application effect is good, which has practical reference significance for other universities.

Digital identity, privacy security, and their legal safeguards in the Metaverse

The Metaverse is the digitization of the real world, supported by big data, AI, 5G, cloud computing, blockchain, encryption algorithm, perception technology, digital twin, virtual engine, and other technologies that interact with human behavior and thoughts in avatars through digital identity. Cracking the trust problem brought by the avatar depends on the privacy security and authentication technology for individuals using digital identities to enter the Metaverse. To accomplish personal domination of the avatar, metaverse users need privacy data feeding and emotion projection. They must be equipped with proprietary algorithms to process and analyze the complex data generated in adaptive interactions, which challenges the privacy security of user data in the Metaverse. Distinguishing the significance of different identifiers in personal identity generation while imposing different behavioral regulatory requirements on data processing levels may better balance the relationship between personal privacy security and digital identity protection and data utilization in the Metaverse. In response to digital identity issues, there is an objective need to establish a unified digital identity authentication system to gain the general trust of society. Further, the remedies for a right to personality can be applied to the scenario of unlawful infringement of digital identity and privacy security.

Preventing Cyber-Fraud in Nigeria’s Banking System Using Fraudaeck-AI

Preventing Cyber-Fraud in Nigeria's Banking System Using Fraudaeck-AI (Fraud analysis environment for cyber-fraud check) is an in-depth analysis of artificial intelligence (AI) assisted identity verification and authentication systems to prevent cyber fraud in Nigeria's banking system. The Fraudaeck (fraud analysis environment for cyber-fraud check) is a machine learning model developed to learn the interconnected subsystems of the communication network and the banking applications and how they function seamlessly to provide customers with ease and comfort of banking even on the go. An investigation revealed how this created a vulnerability in the system, allowing malicious software attacks such as the Xenomorph Trojan to gain access to the system, paving the way for cyber fraudsters—"Yahoo-boys"—to gain access. This paper proposes a solution to cyber-fraud during electronic banking transactions by using an artificial neural network model called Fraudaeck, which can be interfaced with both the telecommunication protocols and the banking application to detect network intrusion, customer identity theft, and prevent cyber-fraud to obtain hitch-free banking activities in Nigeria.

VoIPChain: A decentralized identity authentication in Voice over IP using Blockchain

Confidentiality, availability, integrity, authentication, and non-repudiation when together became the top priority for secure communication. However, authentication is the first line of defense in all these security parameters. Unfortunately, several authentication mechanisms in the traditional methods cannot provide secure communication due to various vulnerabilities such as single point of failure and privacy issues. Furthermore, key distribution mechanisms such as Certificate Authority (CA) and Trusted Third Party (TTP) distribute keys over the central architecture fail, thus secure communication cannot occur. As a solution to these drawbacks, this paper proposes a new decentralized blockchain-based identity authentication mechanism for VoIP networks, VoIPChain. The proposed scheme utilizes the main features of the blockchain platform, such as immutability, transparency, and fault tolerance, to provide data privacy and secure communication in VoIP applications. The proposed scheme is evaluated as an actual implementation using the virtual Ethereum platform and Python language. The experimental results show that the proposed scheme is an efficient and cost-effective solution for the call process as a decentralized identity authentication system in VoIP. In addition, end-to-end secure call performance outperforms the existing blockchain-based authentication mechanisms by 30%–70% in terms of average time delay. The proposed scheme is almost ten times faster than the TLS process to authenticate between parties. Moreover, compared to fast but less secure basic methods over the SIP authentication the proposed scheme has an acceptable time delay in a call process.

Research on identity authentication system of Internet of Things based on blockchain technology

With the development of the Internet of Things (IoT) and its applications, a large amount of data is generated regularly. If this information is used by malicious attackers, it will be a great disaster for the relevant users. In this regard, this article focuses on the user’s identity privacy issues involved in the IoT. By protecting the user’s identity privacy, the attacker cannot associate the obtained data with the user’s real identity, and so achieve the purpose of protecting the user. This article uses the features of blockchain that cannot be tampered with nor forged to strengthen the reliability of the system. The proposed scheme saves the transaction information of user information through the Hyperledger and uses the ring signature method to obscure the real identity. A key generator is used to generate system public parameters and ring membership information required for signature. Users can use this information to hide their identity in a ring group of n users so that other users can only guess the true identity of the user with a probability of 1/n. Additionally, the method of aggregated signature is used to shorten the time and space required for k signature verification to 1/k, which greatly improves the efficiency. Finally, this article also uses an accountability mechanism to punish some attackers who attempt to waste system resources by revealing the real identity of the attacker and refusing to serve him. In this paper, GO language is used to write chain code to realize the proposed algorithm, and a prototype system is built through HyperLeger Fabric blockchain network, and the prototype system is verified by experiment. The correctness and efficiency of the above scheme are also proved through theoretical analysis and experiments.

Analysis of Safe Storage of Network Information Data and Financial Risks Under Blockchain Combined With Edge Computing

To discuss the control of financial risks (FRs) under blockchain (BC) and improve network information security (NIS) and data security, edge computing (EC) combined with BC is proposed to control the risks of the big data (BD) financial system. Firstly, the BC-based financial system is introduced, and the characteristics of BC such as decentralization, tamper-resistant, and smart contract are analyzed. Secondly, the development status of NIS and the characteristics of marginal computing are explained, and the control model of NIS is established. Then, EC is used to encrypt the identity authentication system to ensure data security, and the BC-based FR evaluation model is established. Finally, a questionnaire is designed regarding the NIS model, and the results are analyzed. A simulation experiment is conducted regarding the index evaluation of the BC-based FR evaluation model. The experimental results indicate that network personnel control, environment, and technology have positive effects on NIS, and the impact factors are 0.26, 0.24, and 0.33, respectively.

Application of Face Recognition in E-commerce Security Authentication in the Era of Big Data

Although Internet technology brings invaluable benefits to all walks of life, the security of network and information is becoming more and more prominent. The leakage of personnel information caused by Internet security incidents causes irreparable harm and loss to individuals or enterprises. E-commerce is a virtual transaction mode based on Internet technology. Its security requirements for transactions are more stringent than those for traditional transaction modes. Traditional identity authentication technology can no longer meet its security needs. People urgently need a reliable identity authentication system, meaning to ensure the security of e-commerce. According to the actual application scenarios of the algorithm in e-commerce, this paper, based on the research on face recognition technology, focuses on the in-depth research on face detection technology under the background of big data in order to introduce face recognition technology in this paper. This paper proposes a feature-based face matching algorithm. The face image is preprocessed to improve the accuracy of real-time face detection and reduce the false detection rate. Based on the research of traditional facial feature extraction technology, a virtual sample set that can effectively support a traditional facial feature extraction algorithm is constructed to solve the problem of insufficient training samples in practical applications. The experimental results showed that the accuracy of the method in this paper can reach up to 79.5% and that the minimum time consumption is only 0.142 s. Compared with the traditional method, the accuracy rate is higher and the time consumption is shorter.

Health-zkIDM: A Healthcare Identity System Based on Fabric Blockchain and Zero-Knowledge Proof.

The issue of identity authentication for online medical services has been one of the key focuses of the healthcare industry in recent years. Most healthcare organizations use centralized identity management systems (IDMs), which not only limit the interoperability of patient identities between institutions of healthcare, but also create isolation between data islands. The more important matter is that centralized IDMs may lead to privacy disclosure. Therefore, we propose Health-zkIDM, a decentralized identity authentication system based on zero-knowledge proof and blockchain technology, which allows patients to identify and verify their identities transparently and safely in different health fields and promotes the interaction between IDM providers and patients. The users in Health-zkIDM are uniquely identified by one ID registered. The zero-knowledge proof technology is deployed on the client, which provides the user with a proof of identity information and automatically verifies the user’s identity after registration. We implemented chaincodes on the Fabric, including the upload of proof of identity information, identification, and verification functions. The experiences show that the performance of the Health-zkIDM system can achieve throughputs higher than 400 TPS in Caliper.

Applying Multiple Functional Connectivity Features in GCN for EEG-Based Human Identification.

EEG-based human identification has gained a wide range of attention due to the further increase in demand for security. How to improve the accuracy of the human identification system is an issue worthy of attention. Using more features in the human identification system is a potential solution. However, too many features may cause overfitting, resulting in the decline of system accuracy. In this work, the graph convolutional neural network (GCN) was adopted for classification. Multiple features were combined and utilized as the structure matrix of the GCN. Because of the constant signal matrix, the training parameters would not increase as the structure matrix grows. We evaluated the classification accuracy on a classic public dataset. The results showed that utilizing multiple features of functional connectivity (FC) can improve the accuracy of the identity authentication system, the best results of which are at 98.56%. In addition, our methods showed less sensitivity to channel reduction. The method proposed in this paper combines different FCs and reaches high classification accuracy for unpreprocessed data, which inspires reducing the system cost in the actual human identification system.

Efficient privacy protection authentication protocol for vehicle network in 5G

SummaryThe traditional internet of vehicles (IoV) identity authentication system generally has the security risk that the center is not trusted. In the 5G IoV, a large number of deployed 5G base stations are integrated into it, which has further developed the technologies such as seamless handover, load balancing, and security authentication. The development of the IoV has largely alleviated the traffic pressure and improved the driving efficiency. However, there are many privacy security problems to be solved in 5G IoV. Therefore, with the characteristics of blockchain distribution, traceability, and non‐tampering, an efficient privacy protection authentication protocol based on blockchain is proposed. The method includes two parts: identity registration and authentication. Vehicles and other traffic participants obtain a public identity block through identity registration before accessing the IoV. In the subsequent authentication, the communication parties use this identity block for anonymous authentication. The analysis results based on the formal analysis software Scyther are safe. The security analysis shows that the scheme could meet the security requirements in the IoV. At the same time, the comparison with the existing schemes shows that this scheme has lower computation overhead and communication cost.

Research on the effect of generative adversarial network based on wavelet transform hidden Markov model on face creation and classification

Abstract Identity authentication and identification as the main activities in the modern human daily life, in the development of social economy and technological innovation of science and technology, information security and public security and other fields of identity recognition technology put forward higher request, not only to accurately record the relevant data information, and use of advanced technology concept advanced authentication technology is put forward. Knowing the living habits of current social residents, they need to choose appropriate ways to prove their identity, whether they enter the computer system or participate in financial services. For example, passwords, keys, ID cards and other contents are traditional means of identity authentication. As the reliability of traditional identity authentication means is getting lower and lower in today’s social network environment, in order to meet the needs of increasingly innovative network platforms and enhance the security and reliability of identity authentication technology, researchers propose to use biometric identification, among which face recognition is the most common. This recognition method is more direct and effective than palmprint, retina, fingerprint and so on, and occupies an important position in the current biometric research topic. After understanding the application of hidden Markov model (HMM) based on wavelet transform and its improved model in the field of face authentication, this paper analyzes the influence of various observation vector optimization methods on the authentication probability, and proposes a dynamic threshold method combined with the authentication model, in order to obtain a better authentication probability. The final design and implementation of the face information as the core of the identity authentication system. The improved hidden Markov model can effectively represent the correlation between face organs, so it has a positive role in the application of face modeling and authentication technology.

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.

A blockchain-based lightweight identity authentication scheme for the IEDs of security and stability control system

As an important part of the second defense line of the power system, the Security and Stability Control System (SSCS) is of great significance to ensure the reliable operation of the power system. However, SSCS still lacks an effective security mechanism and is easily accessed by attackers, thus posing a threat to the stable and reliable operation of the power system. To tackle this issue, we propose a blockchain-based identity authentication scheme for Intelligent Electronic Devices (IEDs) of SSCS. We first propose an identity authentication system model for IEDs and design the deployment of consortium chain nodes on IEDs, with architectural characteristics of SSCS and the working scenario of IEDs taken into consideration. The consortium chain is used to store credentials required for authentication, ensuring that they are tamper-proof. We combine IP address, port number and physical ID, and propose the unique identification of IEDs, with a data structure designed for the identification. We also propose a lightweight identity authentication method based on renewable hash chains, with hash chains used as one-time authentication passwords, and introduce a renewal mechanism of hash chains. Further, the detailed processes of registration and authentication phase are designed. Finally, the security analysis shows that our identity authentication scheme can resist various attacks, and the feasibility of our scheme is verified by experiments.

Using electroencephalogram classification in a convolutional neural network, infer privacy on healthcare internet of things 5.0

AbstractWhen enabled by the internet of health things (IoHT), brain neuroscience may conduct online analysis of brain information through multi‐variate electroencephalogram (EEG) classification, which would be a requirement for the recent surge in biofeedback technologies and medical supervision. With the ever‐increasing privacy issues and vulnerabilities of conventional methods, a universal and reliable‐based authentication framework for smart IoHT application with 5G technology (healthcare 5.0) is needed. Research teams have come to trust the EEG features because of their reliability, durability and universality. Fortunately, the testing paradigm's restricted functionality and poor classification accuracy have kept an EEG‐based identity authentication scheme from being widely seen in IoHT scenarios. However, due to unsatisfactory categories and the failure of a reliable identity authentication scheme, it remains important in research challenges. This research presents the design of an EEG identity authentication system supported via convolutional neural network classification includes cloud support storage methodology in the healthcare 5.0 environment, resulting in extremely high reliability, consistency and protection for the next generation of smart systems. The experimental results indicate that the accuracy and efficacy of the user authentication expect a higher legal probability of success and a lower unauthorized likelihood of success from a safety perspective. As compared to other frameworks, traditional EEG‐based authorization approaches test results to reveal that the proposed methodology yields the desired classification accuracy of 97.6%. The experiment performance on an authentication scenario is structured to prove that the proposed method is efficient, reliable and accurate.

A Lightweight Face Verification Based on Adaptive Cascade Network and Triplet Loss Function

In the past few years, with the continuous breakthrough of technology in various fields, artificial intelligence has been considered as a revolutionary technology. One of the most important and useful applications of artificial intelligence is face detection. The outbreak of COVID-19 has promoted the development of the noncontact identity authentication system. Face detection is also one of the key techniques in this kind of authentication system. However, the current real-time face detection is computationally expensive which hinders the application of face recognition. To address this issue, we propose a face verification framework based on adaptive cascade network and triplet loss. The framework is simple in network architecture and has light-weighted parameters. The training network is made of three stages with an adaptive cascade network and utilizes a novel image pyramid based on scales with different sizes. We train the face verification model and complete the verification within 0.15 second for processing one image which shows the computation efficiency of our proposed framework. In addition, the experimental results also show the competitive accuracy of our proposed framework which is around 98.6%. Using dynamic semihard triplet strategy for training, our network achieves a classification accuracy of 99.2% on the dataset of Labeled Faces in the Wild.

Blockchain-Based Identity Management System and Self-Sovereign Identity Ecosystem: A Comprehensive Survey

Identity Management System (IDMS) refers to how users or individuals are identified and authorized to use organizational systems and services. Since traditional identity management and authentication systems rely heavily on a trusted central authority, they cannot mitigate the effects of single points of failure. As a decentralized and distributed public ledger in a peer-to- peer (P2P) network, Blockchain (BC) technology has garnered a considerable amount of attention in the field of IDMS in recent years. Through Self-Sovereign Identity (SSI), users can have full authority over their digital identity. Successful implementation of a BC-based IDMS can significantly increase the degree of privacy and security of a user’s SSI. However, the integration of BC-based IMDS to provide a user with SSI is still an unorganized area of research in its early stages of development. This article presents an extensive literature review of state-of-the-art academic publications as well as commercial market offerings regarding the applicability of BC-based SSI solutions. It also provides a detailed preliminary regarding the building blocks of blockchain technology and a progressive roadmap of IDMS solutions. In order to develop an effective BC-based IDMS solution that focuses on securing a user’s SSI, this article outline five essential components of a BC-based IDMS: authentication, integrity, privacy, trust, and simplicity. Furthermore, we perform a security analysis that outlines several types of adversarial threats that can cause potential damage to the BC-based IDMS. We identify and discuss associated issues and challenges by analyzing several notable BC-based IDMS solutions in academic literature. We also highlight potential research gaps and provide future research scope.