Identity Authentication Research Articles

Research on security access authentication mechanism of intelligent sensor based on non-interactive zero-knowledge proof method

In order to study the standard security access authentication mechanism of intelligent sensing terminals of massive power Internet of Things, In order to study the standard secure access authentication mechanism of intelligent sensing terminal of massive power Internet of Things, a new privacy protection method widely used in block chain is proposed to prove identity. The traditional power IoT cloud-side interaction security access MQTT protocol still has a lot of room for adaptation and optimization. First, the proposed non-interactive zero-knowledge proof identity authentication method reduces the time of traditional standard secure access authentication process; Second, it reduced the computing resources consumed in a large number of intelligent sensors access authentication. The comparison results show that, the access authentication time of this method is 30%∼50% less than that of the traditional secure access authentication process. The computing resources consumed during authentication are reduced by 20% to 30% compared with traditional security and secrecy mechanisms.

Data-Augmentation-Enabled Continuous User Authentication via Passive Vibration Response

Continuous identity authentication is critical for privacy protection throughout an entire user login session. In this paper, we propose a continuous user authentication mechanism namely, which employs the vibration responses from hand biometrics and is passively activated by natural user-device interaction. Hand vibration responses are embedded in the mechanical vibration of a force-bearing body consisting of one mobile device and one user hand. A built-in accelerometer of the device can capture hand-dependent vibration signals. Considering the concealment of vibration generation and the non-replicability of hand structure, it’s difficult for attackers to counterfeit user identity. Moreover, for ensuring the robustness of authentication performance to tapping behavior interference, we construct a data augmentation module jointly leveraging a signal processing and learning-based pipeline. It can generate enough vibration responses representing hand structure biometrics under various behaviors, thereby making comprehensively understand vibration response variation. We prototype on smartphones, and extensive experiments demonstrate that can achieve satisfactory authentication accuracy.

Design of a Decentralized Identifier-Based Authentication and Access Control Model for Smart Homes

In recent years, smart homes have garnered extensive attention as a prominent application scenario of IoT technology. However, the unique characteristics of smart homes have brought forth serious security threats, emphasizing the paramount importance of identity authentication and access control. The conventional centralized approach is plagued by the issue of having a “single point of failure,” while existing distributed solutions are constrained by limited device resources and the complexities of identity authentication. To tackle these challenges, this paper proposes a smart home authentication and access control model based on decentralized identifiers (DIDs). By leveraging the inherent decentralization of DIDs, which rely on blockchain, a distributed environment is constructed, effectively mitigating the problem of the “single point of failure.” In this model, every participant in the smart home system, including users and smart devices, is uniquely identified by DIDs and through the integration of an improved capability-based access control scheme, which streamlines the user identity authentication process, reduces authentication complexity, and enables convenient cross-household access with a single registration. Our experimental results demonstrate that the application of decentralized identifiers provides the model with various security attributes, including confidentiality, integrity, and traceability. Additionally, the model exhibits low time costs for each module, ensuring timely responses to access service requests and incurring lower gas consumption compared to other Ethereum-based methods. Thus, our research proposes a lightweight authentication and access control solution suitable for smart home environments.

Machine Learning-Assisted Watermarking via Bias Control of Electro-Optical Modulator

We propose an optical watermarking strategy based on precise bias tuning of an electro-optical modulator and a convolutional neural network-enabled decoder. This scheme can be used for identity authentication at the physical layer of optical networks. This method is demonstrated with a transmission demo of about 250 kbit/s watermarking data in a 10-Gbit/s public 20-km communication. The watermarking data is modulated on the bias states of the modulator based on the precise and stable bias control technique. The watermark embedding has been verified to make almost no impact on the 20-km public transmission. For the legal receiver, the invisible watermark data can be extracted with the aid of the convolutional neural network-enabled decoder to verify the legality of the received data. Only one optical source is needed in the proposed scheme and the watermarking is integrated into the public signal naturally. We analyzed and verified the influence of watermarking data on the bit error rate and the channel capacity of the public data. The privacy and security of watermarking data are also analyzed.

Quantum identity authentication for non-entanglement multiparty communication: A review, state of art and future directions

Quantum identity authentication for non-entanglement multiparty communication: A review, state of art and future directions

Encrypt with Your Mind: Reliable and Revocable Brain Biometrics via Multidimensional Gaussian Fitted Bit Allocation.

Biometric features, e.g., fingerprints, the iris, and the face, have been widely used to authenticate individuals. However, most biometrics are not cancellable, i.e., once these biometric features are cloned or stolen, they cannot be replaced easily. Unlike traditional biometrics, brain biometrics are extremely difficult to clone or forge due to the natural randomness across different individuals, which makes them an ideal option for identity authentication. Most existing brain biometrics are based on electroencephalogram (EEG), which is usually demonstrated unstable performance due to the low signal-to-noise ratio (SNR). For the first time, we propose the use of intracortical brain signals, which have higher resolution and SNR, to realize the construction of the high-performance brain biometrics. Specifically, we put forward a novel brain-based key generation approach called multidimensional Gaussian fitted bit allocation (MGFBA). The proposed MGFBA method extracts keys from the local field potential of ten rats with high reliability and high entropy. We found that with the proposed MGFBA, the average effective key length of the brain biometrics was 938 bits, while achieving high authentication accuracy of 88.1% at a false acceptance rate of 1.9%, which is significantly improved compared to conventional EEG-based approaches. In addition, the proposed MGFBA-based keys can be conveniently revoked using different motor behaviors with high entropy. Experimental results demonstrate the potential of using intracortical brain signals for reliable authentication and other security applications.

A User Authentication Schema Under the Integration of Mobile Edge Computing and Blockchain Technology

The safe and efficient authentication of users is the basis for the realization of the Internet of Things. A reliable and fast user authentication schema that combines mobile edge computing and blockchain technology is proposed. This schema adopts the polling hosting method to optimize the practical Byzantine Fault Tolerance (PBFT) algorithm, it reduces the impact of tokens in the algorithm, and ensures trusted authentication. The two-way authentication protocol between the cluster head and the base station, and the one-way authentication protocol between the base station and the sensor node are designed to effectively simplify the authentication process of sensor nodes, which further guarantees the security and speed of user authentication and effectively meets the security, reliability, and convenience requirements of the Internet of Things. Simulation experiments show that the schema can achieve efficient information verification, and the identity authentication time and protocol authentication times are only 23.58 ms and 25.06 ms, respectively, which has obvious performance advantages over the Paillier algorithm, the hybrid Paillier-blowfish algorithm, and the ElGamal algorithm.

Microcontroller-Based PUF for Identity Authentication and Tamper Resistance of Blockchain-Compliant IoT Devices.

Blockchain-based applications necessitate the authentication of connected devices if they are employed as blockchain oracles. Alongside identity authentication, it is crucial to ensure resistance against tampering, including safeguarding against unauthorized alterations and protection against device counterfeiting or cloning. However, attaining these functionalities becomes more challenging when dealing with resource-constrained devices like low-cost IoT devices. The resources of IoT devices depend on the capabilities of the microcontroller they are built around. Low-cost devices utilize microcontrollers with limited computational power, small memory capacity, and lack advanced features such as a dedicated secure cryptographic chip. This paper proposes a method employing a Physical Unclonable Function (PUF) to authenticate identity and tamper resistance in IoT devices. The suggested PUF relies on a microcontroller's internal pull-up resistor values and, in conjunction with the microcontroller's built-in analog comparator, can also be utilized for device self-checking. A main contribution of this paper is the proposed PUF method which calculates the PUF value as the average value of many single PUF measurements, resulting in a significant increase in accuracy. The proposed PUF has been implemented successfully in a low-cost microcontroller device. Test results demonstrate that the device, specifically the microcontroller chip, can be identified with high accuracy (99.98%), and the proposed PUF method exhibits resistance against probing attempts.

Efficient ECC-Based Authentication Scheme for Fog-Based IoT Environment

The rapid growth of cloud computing and Internet of Things (IoT) applications faces several threats, such as latency, security, network failure, and performance. These issues are solved with the development of fog computing, which brings storage and computation closer to IoT-devices. However, there are several challenges faced by security designers, engineers, and researchers to secure this environment. To ensure the confidentiality of data that passes between the connected devices, digital signature protocols have been applied to the authentication of identities and messages. However, in the traditional method, a user's private key is directly stored on IoTs, so the private key may be disclosed under various malicious attacks. Furthermore, these methods require a lot of energy, which drains the resources of IoT-devices. A signature scheme based on the elliptic curve digital signature algorithm (ECDSA) is proposed in this paper to improve the security of the private key and the time taken for key-pair generation. ECDSA security is based on the intractability of the Elliptic Curve Discrete Logarithm Problem (ECDLP), which allows one to use much smaller groups. Smaller group sizes directly translate into shorter signatures, which is a crucial feature in settings where communication bandwidth is limited, or data transfer consumes a large amount of energy. In this paper, we have chosen the safe curve types of elliptic-curve cryptography (ECC) such as M221, SECP256r1, curve 25519, Brainpool P256t1, and M-551. These types of curves are the most secure curves of other curves of ECC as their security is based on the complexity of the ECDLP of the curve. And these types of curves exceed the complexity of the ECDLP. A valid signature can be generated without reestablishing the whole private key. ECDSA ensures data security and successfully reduces intermediate attacks. The efficiency and effectiveness of ECDSA in the IoT environment are validated by experimental evaluation and comparison analysis. The results indicate that, in comparison to the two-party ECDSA and RSA, the proposed ECDSA decreases computation time by 65% and 87%, respectively. Additionally, as compared to two-party ECDSA and RSA, respectively, it reduces energy consumption by 77% and 82%.

Finger Vein Recognition Technology: Principles, Applications, and Future Prospects

Finger vein recognition technology is a biometric identification technique that analyzes the vein patterns in the fingers to authenticate individuals. This paper aims to provide a comprehensive overview of the principles, applications, and future prospects of finger vein recognition technology. Firstly, the unique biological characteristics and advantages of finger vein recognition will be explored. Secondly, the working principle and key techniques of the finger vein recognition system will be introduced, covering image acquisition, feature extraction, and matching algorithms. Subsequently, the extensive applications of finger vein recognition technology in personal identity authentication, financial payments, information security, and other domains will be discussed. Finally, the future development of finger vein recognition technology will be forecasted, addressing potential challenges and solutions, as well as its potential applications in broader domains.

Replay attack detection based on deformable convolutional neural network and temporal-frequency attention model

Abstract As an important identity authentication method, speaker verification (SV) has been widely used in many domains, e.g., mobile financials. At the same time, the existing SV systems are insecure under replay spoofing attacks. Toward a more secure and stable SV system, this article proposes a replay attack detection system based on deformable convolutional neural networks (DCNNs) and a time–frequency double-channel attention model. In DCNN, the positions of elements in the convolutional kernel are not fixed. Instead, they are modified by some trainable variable to help the model extract more useful local information from input spectrograms. Meanwhile, a time–frequency domino double-channel attention model is adopted to extract more effective distinctive features to collect valuable information for distinguishing genuine and replay speeches. Experimental results on ASVspoof 2019 dataset show that the proposed model can detect replay attacks accurately.

LCDMA: Lightweight Cross-Domain Mutual Identity Authentication Scheme for Internet of Things

With the widespread popularity of mobile terminals in the Internet of things (IoT), the demand for cross-domain access of mobile terminals between different regions has also increased significantly. The nature of wireless communication media makes mobile terminals vulnerable to security threats in cross-domain access. Identity authentication is a prerequisite for secure data transmission in cross-domain, and it is also the first step to guarantee the credibility of data sources. Most existing authentication schemes are based on bilinear pairing or public key encryption and decryption with high computation overhead, which are not suitable for the resource-limited mobile IoT terminals. Moreover, these schemes have some security drawbacks and cannot meet the security requirements of cross-domain access. In this paper, we propose a lightweight cross-domain mutual identity authentication (LCDMA) for mobile IoT environment. LCDMA uses symmetric polynomial instead of high-complexity bilinear pairing in the traditional schemes. We theoretically analyze the security performance under the random oracle model. Our results show that LCDMA not only resists common attacks, but also preserves secure traceability while guaranteeing anonymity. Performance evaluation further demonstrates that our scheme has better performance in terms of computation and communication overhead, compared with other existing representative schemes.

Study of fingerprint patterns in relation to gender and blood groups

Dermatoglyphics (fingerprint/dactylography) is derived from the Greek word “Derma=Skin, Glyph=Carve.” Dermatoglyphics is defined as the scientific study of naturally occurring epidermal ridges and their configuration on the digits, palms and soles apart from flexion crease and secondary folds. The objective of the study was to determine correlation if any exits between the fingerprint pattern and blood group and gender. The general study design selected for this article was cross-sectional study. Here, the primary outcome variable is to determine correlation (if any) between the fingerprint pattern and blood group and gender. A total of 271 students volunteered for the study. Loops were found to the most prevalent type of fingerprint in all blood groups. Blood groups and primary fingerprint patterns were found to be associated with the association being statistically significant. The correlation between the said entities would enable better authentication and prediction of identity of a person and thereby has the potential for great application in the field of forensics.

BDRA: Blockchain and Decentralized Identifiers Assisted Secure Registration and Authentication for VANETs

In vehicular ad-hoc networks (VANETs), road safety and road traffic efficiency can be improved through message interaction between vehicle users, which inevitably relies on secure identity authentication and message credibility verification. Existing authentication and message verification mechanisms are prone to severe single points of failure and low authentication efficiency due to their reliance on the trusted third party, especially during the user registration phase. This paper proposes a double-layer blockchain and decentralized identifiers assisted secure registration and authentication (BDRA) mechanism for decentralized VANETs, which can achieve the following advantages: 1) realizing a secure and decentralized user registration phase by using decentralized identifier (DID) technology; 2) accomplishing efficient authentication and message verification by combining double-layer blockchain, DIDs and a reputation feedback strategy; 3) enabling a more efficient cross-section re-registration that reduces the communication time by 30%. The security features and efficiency of the BDRA mechanism are demonstrated by carrying out security analysis and performance evaluation, which is based on the Hyperledger Fabric platform.

Deep-Reinforcement-Learning-Based Wireless IoT Device Identification Using Channel State Information

Internet of Things (IoT) technology has permeated into all aspects of today’s society and is playing an increasingly important role. Identity authentication is crucial for IoT devices to access the network, because the open wireless transmission environment of the IoT may suffer from various forms of network attacks. The asymmetry in the comprehensive capabilities of gateways and terminals in the IoT poses significant challenges to reliability and security. Traditional encryption-based identity authentication methods are difficult to apply to IoT terminals with limited capabilities due to high algorithm complexity and low computational efficiency. This paper explores physical layer identity identification based on channel state information (CSI) and proposes an intelligent identification method based on deep reinforcement learning (DRL). Specifically, by analyzing and extracting the features of the real received CSI information and a setting low-complexity state, as well as action and reward parameters for the deep neural network of deep reinforcement learning oriented to the scenario, we obtained an authentication method that can efficiently identify identities. The validation of the proposed method using collected CSI data demonstrates that it has good convergence properties. Compared with several existing machine-learning-based identity recognition methods, the proposed method has higher recognition accuracy.

DKSM: A Decentralized Kerberos Secure Service-Management Protocol for Internet of Things

Kerberos is a widely used authentication protocol that protects distributed services on the Internet of Things (IoT) and big data. In a distributed scenario, entities must prove their identity to a trusted third party using shared secrets, such as secret keys. Traditional schemes typically use a trusted central organization, like a Key Distribution Center, for identity authentication. However, Kerberos has some downsides, such as a single point of failure, vulnerability to replay attacks, and potential credential exposure, which can compromise system security. To address these problems, researchers have been working on various solutions, but most have their own drawbacks. In this paper, we propose a Decentralized Kerberos Secure Service-Management Protocol (DKSM) based on blockchain technology and Ciphertext-policy Attribute-based Encryption (CP-ABE) schema. Compared with existing protocols, DKSM fulfills decentralization, fine-grained access control with effective cost, and scalability simultaneously. DKSM uses AES and Fast Attribute-Based Encryption with Optimal Security (FABEO) as its cryptographic basis. We also discuss the security of DKSM and demonstrate how our protocol can defend against attacks. Finally, tests on the Ethereum testnet and FISCO consortium platform have shown that our designed protocol is efficient and cost-effective.

Powwow Regalia in Identity Performance and Authentication

AbstractThrough personal narratives of powwow involvement and motivation for dancing, this essay examines the ways in which regional and personal identities are being formed, adjusted, negotiated, and expressed through dance regalia at powwows in the Midwestern United States. Dancers use clothes as an explicit marker of their Native identity and powwows as a justifying context for their ideologies of authenticity. Powwow involvement is also used to consolidate, reclaim, craft, revive, and create an identity that authenticates one's place in the powwow community in which internal and external roles and rules reinforce each other. Giving voice to different constituents at Midwestern powwows, from Natives to non-Native enthusiasts, the study explores the factors that influence the bases and strategies of such authentication, as well as the rhetoric by which these ideologies are expressed.

Optimal Feature Analysis for Identification Based on Intracranial Brain Signals with Machine Learning Algorithms.

Biometrics, e.g., fingerprints, the iris, and the face, have been widely used to authenticate individuals. However, most biometrics are not cancellable, i.e., once these traditional biometrics are cloned or stolen, they cannot be replaced easily. Unlike traditional biometrics, brain biometrics are extremely difficult to clone or forge due to the natural randomness across different individuals, which makes them an ideal option for identity authentication. Most existing brain biometrics are based on an electroencephalogram (EEG), which typically demonstrates unstable performance due to the low signal-to-noise ratio (SNR). Thus, in this paper, we propose the use of intracortical brain signals, which have higher resolution and SNR, to realize the construction of a high-performance brain biometric. Significantly, this is the first study to investigate the features of intracortical brain signals for identification. Specifically, several features based on local field potential are computed for identification, and their performance is compared with different machine learning algorithms. The results show that frequency domain features and time-frequency domain features are excellent for intra-day and inter-day identification. Furthermore, the energy features perform best among all features with 98% intra-day and 93% inter-day identification accuracy, which demonstrates the great potential of intracraial brain signals to be biometrics. This paper may serve as a guidance for future intracranial brain researches and the development of more reliable and high-performance brain biometrics.

Research on Identity Authentication Scheme for UAV Communication Network

Unmanned aerial vehicles (UAV) play a vital role in many fields, such as agricultural planting, security patrol, emergency rescue, and so on. The development and implementation of these devices have become vital in terms of reachability and usability. Unfortunately, as drones become more widely used in various fields, they become more and more vulnerable to attacks and security threats, including, but not limited to, eavesdropping, man-in-the-middle attacks, and known session key attacks. In order to deal with these attacks and security threats and meet the needs of lightweight UAV communication, a secure and efficient authentication scheme is essential. To meet the security and lightweight requirements of an identity authentication scheme in a UAV communication network, this paper proposes an identity authentication scheme sdronelig based on an elliptic curve cryptosystem. The scheme realizes the mutual authentication and session key agreement configuration between the UAV and the ground station, and the authentication and key agreement between the UAVs can be realized with the help of the control station. The sdronelig authentication scheme is based on the ECDH key exchange protocol in the elliptic curve cryptography algorithm and adopts the MAC message authentication code technology and the method of pre-calculating part of the process. Under the premise of ensuring the security of the UAV communication network, the authentication efficiency is improved, the communication overhead and communication times are reduced, and the lightweight requirement of the UAV authentication scheme is met. Additionally, a formal verification tool is used to verify the security of the sdronelig scheme under the Dolev-Yao threat model, which is suitable for UAV networks. Finally, a detailed comparative study was conducted on security features, communication overhead, the number of communications, and computational overhead. The results show that the proposed sdronelig authentication scheme not only provides superior security features but also has better or comparable overhead compared to other existing authentication schemes.

Trusted edge and cross-domain privacy enhancement model under multi-blockchain

With its promising security and distributed qualities, blockchain offers a significant opportunity to break through the privacy protection issues in the edge computing paradigm. However, when edge participants submit security tasks to a distributed blockchain network across domains, they may expose their data privacy and location privacy. These privacy will be maliciously attacked and exploited by attackers such as external attackers and untrusted third parties, increasing the difficulty of access control. In addition, the performance bottleneck of blockchain systems cannot meet the demand for efficient data processing. To address these problems, we propose a cross-domain privacy enhancement scheme based on multi-blockchain. The scheme first integrates edge computing based on master-slave multi-blockchain and designs an identity authentication mechanism and trust assessment mechanism to deploy a three-layer trusted network architecture, which ensures the security stability and operational efficiency at the edge side. Secondly, role mapping rules are developed and trust degree is evaluated for domain nodes, and a cross-domain access control model based on trust degree and role is proposed. Data in this model cannot be exchanged across domains until all nodes jointly verify whether the access control policy is effective, which ensures the secure sharing of data in heterogeneous domains. In addition, to ensure the privacy and authenticity of data in cross-domain sharing, a hybrid searchable encryption method based on symmetric encryption and public key encryption is proposed. Finally, the security theoretical analysis proves that the model successfully ensures the non-repudiation of access control policy and the controllability of cross-domain data. Experiments show that the proposed model improves access dynamics by 74.8% and reduces CPU usage by 24.6% on average compared to traditional RBAC. The scheme is scalable, and its sending rate to throughput ratio reaches 1:1, gaining a 2X throughput advantage over existing schemes.