Secure Authentication System Research Articles

Reliable Multi-Factor User Authentication With One Single Finger Swipe

Multi-factor user authentication becomes increasingly popular due to its superior security comparing with single-factor user authentication. However, existing multi-factor user authentication methods usually require multiple interactions between users and different authentication components when inputting the multiple factors, leading to extra overhead and bad user experience. In this paper, we propose a secure and user-friendly multi-factor user authentication system named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BioDraw</i> . It utilizes four categories of biometrics (impedance, geometry, behavior, and composition) of human hand plus the pattern-based password to identify and authenticate users. User only needs to draw a pattern on a radio frequency identification tag array, while four biometrics can be collected simultaneously. Specifically, we first design a gradient-based pattern recognition algorithm to precisely extract user’s secret pattern. Then, a convolutional neural network-and long short-term memory-based classifier is utilized for user recognition. Furthermore, to guarantee the systemic security, an anti-replay method called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Binary ALOHA</i> is proposed to detect replayed signals. We conduct extensive experiments with 30 volunteers. The experiment results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BioDraw</i> can achieve high authentication accuracy (with a 2% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$-$</tex-math> </inline-formula> false reject rate) and is effective in defending against various attacks.

Morse Code Based Secured Authentication System through Machine Learning

Data science is a multidisciplinary blend of data inference, algorithm development and technology in order to solve analytically complex problems. Data science is used by almost all the industries like instructive organizations, finance, medical services, business to deal with huge volumes of information. The pragmatic applications range from foreseeing stock development to anticipating disease; utilized in picture preparing to character acknowledgment, sound handling for discourse to message expectation. Since the majority of individuals on the planet are dealing with issues in the field of verification and security. The system provides a real time eye tracing for password authentication for people who authenticate themselves using Morse code. Advancement in the technology of authentication and authorization has be supported in the 21st century a lot as we know. Personal identification numbers (PIN) are widely used for user authentication and security since the late 90's. Since PIN numbers are easily crack able these days, people prefer to follow different approach. PIN validation with hands-off look-based PIN section procedures, then again, abandons no actual impressions and in this manner offers a safer secret word passage alternative. Gaze-based system for authentication alludes to discovering the eye area across consecutive picture frames and following the eye movements by plotting the eye center. Password authentication will be done using Morse code, where numbers will be represented in dots and dashes. This model presents a real-time application for gaze-based PIN entry with face recognition, and eye detection and tracking for PIN identification using a smart camera

Rhythmic RFID Authentication

Passive RFID technology is widely used in user authentication and access control. We propose RF-Rhythm, a secure and usable two-factor RFID authentication system with strong resilience to lost/stolen/cloned RFID cards. In RF-Rhythm, each legitimate user performs a sequence of taps on his/her RFID card according to a self-chosen secret melody. Such rhythmic taps can induce phase changes in the backscattered signals, which the RFID reader can detect to recover the user’s tapping rhythm. In addition to verifying the RFID card’s identification information as usual, the backend server compares the extracted tapping rhythm with what it acquires in the user enrollment phase. The user passes authentication checks if and only if both verifications succeed. We also propose a novel phase-hopping protocol in which the RFID reader emits Continuous Wave (CW) with random phases for extracting the user’s secret tapping rhythm. Our protocol can prevent a capable adversary from extracting and then replaying a legitimate tapping rhythm from sniffed RFID signals. Comprehensive user experiments confirm the high security and usability of RF-Rhythm with false-positive and false-negative rates close to zero.

SoundID: Securing Mobile Two-Factor Authentication via Acoustic Signals

Mobile two-factor authentication (TFA), which uses mobile devices as a second security layer of protection to online accounts, has been widely applied with the proliferation of mobile phones. Currently, many studies propose to use acoustic fingerprints as the second factor. However, these solutions ignore the variations of the extracted static acoustic fingerprints incurred by the acoustic propagation process, which we show can be leveraged to develop an enhanced man-in-the-middle (MITM) attack to compromise the security strength of these systems, while hiding the traces of the attacking devices. To address this newly-uncovered vulnerability, we propose SoundID, a secure and novel authentication system that introduces a dual challenge-response design through the acoustic signals of the enrolled phone and the login device. Specifically, the enrolled phone first evaluates its proximity to the login device by the similarity of their audio recordings, and then the login authentication server compares the calculated dynamic acoustic fingerprint with the one received from the enrolled phone. To the best of our knowledge, SoundID is the first scheme that extracts dynamic acoustic fingerprints and can effectively defend against the enhanced MITM attack. SoundID combines the benefits of unpredictable influencing factors of acoustic propagation processes and the stable frequency response of the acoustic hardware, whose high complexity prevents attackers from predicting or impersonating them. We build a prototype of SoundID with off-the-shelf smartphones to validate its robustness and effectiveness. Our results show that SoundID is user-friendly and achieves over 96.62% accuracy with an equal error rate around 4.27%.

MmSafe: A Voice Security Verification System Based on Millimeter-Wave Radar

With the increasing popularity of smart devices, users can control their mobile phones, TVs, cars, and smart furniture by using voice assistants, but voice assistants are susceptible to intrusion by outsider speakers or playback attacks. In order to address this security issue, a millimeter-wave radar-based voice security authentication system is proposed in this paper. First, the speaker's fine-grained vocal cord vibration signal is extracted by eliminating static object clutter and motion effects; second, the weighted Mel Frequency Cepstrum Coefficients (MFCCs) are obtained as biometric features; and finally, text-independent security authentication is performed by the WMHS (Weighted MFCCs and Hog-based SVM) method. This system is highly adaptable and can authenticate designated speakers, resist intrusion by other unspecified speakers as well as playback attacks, and is secure for smart devices. Extensive experiments have verified that the system achieves a 93.4% speaker verification accuracy and a 5.8% miss detection rate for playback attacks.

Secure authentication and privacy-preserving to improve video streaming vehicle ad-hoc network

In vehicular ad hoc networks (VANET), the privacy of vehicle data symbolizes a big challenge towards malicious attacks. On the other side, vehicles in VANET can play a staple role in monitoring the environment by sensing the surrounding environment, compute the sensing information, and transfer the results if needed to the authorized party. Most of the modern VANETs systems encrypt the information to prevent hacking it but mostly neglect the decryption that occurred when data need to re-processed. In this paper, we try to cover this weak point by using fully homomorphic encryption (FHE) because of its specifications. The proposed work focus on twofold: first, create secure authentication and permission management system. While the second is to preserve the privacy of vehicle data that transferred among VANET infrastructure. This scheme also deals with metric security features, such as data privacy, data integrity, and key management. In the experimental results, there is good advance in the fields of interest comparing with the related works.

Design of Multiple Share Creation with Optimal Signcryption based Secure Biometric Authentication System for Cloud Environment

Biometric authentication plays a vital role in the cloud computing (CC) environment to accomplish security and privacy. Several kinds of biometrics are used for the authentication process, particularly, the fingerprint is a commonly used biometric, which accomplishes maximum security. But it can be easily stolen, unintentionally revealed, or difficult to remember. Therefore, biometric authentication techniques are needed and an encryption process is included to improve the security of the authentication process. In this aspect, this paper proposes a multiple share creation with an optimal signcryption-based biometric authentication system (MSCOS-BAS- FLNN- PeSeOA) for the cloud environment. The proposed model enables the creation of multiple shares of the input biometric image (fingerprint) and encrypts them for a secure authentication system. In addition, the encryption of individual shares takes place with an optimal signcryption system where the key generation process is carried out using the penguin search optimization algorithm (PeSeOA). Finally, the functional link neural network (FLNN) model is applied for the biometric authentication verification process. A wide range of simulation analyses are performed to highlight the supreme efficacy of the MSCOS-BAS- FLNN- PeSeOA technique. The experimental outcomes pointed out the hopeful performance of the MSCOS-BAS- FLNN- PeSeOA technique over the recent techniques.

Upgrading Information Security and Protection for Palm-Print Templates

Biometric systems proven to be one of the most reliable and robust method for human identification. Integration of biometrics among the standard of living provokes the necessity to vogue secure authentication systems. The use of palm-prints for user access and authentication has increased in the last decade. To give the essential security and protection benefits, conventional neural networks (CNNs) has been bestowed during this work. The combined CNN and feature transform structure is employed for mapping palm-prints to random base-n codes. Further, secure hash algorithm (SHA-3) is used to generate secure palm-print templates. The proficiency of the proposed approach has been tested on PolyU, CASIA and IIT-Delhi palm-print datasets. The best recognition performance in terms of Equal Error Rate (EER) of 0.62% and Genuine Acceptance Rate (GAR) of 99.05% was achieved on PolyU database.

Face and Voice Authentication System

Abstract: The issue of design and security is very predominant in any financial and business organization, especially such organization as a bank. Therefore, we intend to aid in security of the bank by bringing in an Artificial intelligence system that involves an individual to get an access to some items using face and voice recognition security system. This AI system is not just a normal password lock system that require a user to insert password and gain access to some items, it is a system that has an administrative authentication. In addition, with this kind of security authentication system we intend to implement, a highly secured AI feature, which enables the user with assured and highly secured transactions using their personal frame. Here an individual have to provide the face and voice authentication, which uses different algorithms, and is read by the AI server for clarification and verification. From this project, we hope to build an alternative and highly verified security for banks. Keywords: Artificial intelligence, administrative authentication, secured transactions, financial, business, organization.

Robust Authentication System with Privacy Preservation of Biometrics

IoT-based multi-biometric system is a blend of multiple biometric templates that can be used for user authentication/verification using sensors. The leakage of the biometric trait information may cause critical privacy and security issues. It is expected to protect the privacy details of individuals through the irreversibility, unlinkability, and renewability of multi-biometric templates used in the authentication system. This study presents a robust authentication system with secure multi-biometric template protection techniques based on discrete cosine transform feature transformation and Lagrange’s interpolation-based image transformation. Three biometric traits namely iris, fingerprint, and palm print are recorded using sensors to validate the proposed multi-biometric template protection system. The fusion of all traits used is giving an average of 95.42% genuine acceptance rate and an average of 4.57% false rejection rate. Despite any number of biometric templates used for authentication, the proposed image transformation techniques keep the size of the final storage requirement as 8 X 8, which achieves constant space complexity (O(1)). The stored template is not linked with original templates; it is irreversible and renewable as new enrolment of the same individual will produce a new template every time. Overall, the proposed technique provides a secure authentication system with high accuracy, a constant size database, and the privacy preservation of biometric traits.

Face Security Authentication System Based on Deep Learning and Homomorphic Encryption

The development of deep learning technology has promoted the wide application of face recognition in many scenarios such as mobile payment and social media, but the security of user data is facing great challenges. To protect the privacy of users, face authentication cannot be operated in plaintext. To solve this problem, a face feature ciphertext authentication scheme based on homomorphic encryption is proposed. First, the face image feature extraction is completed based on a deep learning model. Second, the face features are packaged into ciphertext by using homomorphic encryption and batch processing technology, and the face feature ciphertext is saved in the database of the cloud server. Third, combined with automorphism mapping and Hamming distance, a face feature ciphertext recognition method is designed, which can complete face recognition in the case of ciphertext. Finally, the integrity and consistency of face feature ciphertext recognition results before and after decryption are guaranteed by the one-time MAC authentication method. The whole framework can finish identity recognition without decrypting face feature coding, and the homomorphic ciphertext of face feature coding is saved in the database, so there is no risk of face feature coding leakage. Experiments show that the system has met the requirements of real application scenarios.

A Framework for Verification in Contactless Secure Physical Access Control and Authentication Systems

Biometrics is one of the very popular techniques in user identification for accessing institutions and logging into attendance systems. Currently, some of the existing biometric techniques such as the use of fingerprints are unpopular due to COVID-19 challenges. This paper identifies the components of a framework for secure contactless access authentication. The researcher selected 50 journals from Google scholar which were used to analyze the various components used in a secure contactless access authentication framework. The methodology used for research was based on the scientific approach of research methodology that mainly includes data collection from the 50 selected journals, analysis of the data and assessment of results. The following components were identified: database, sensor camera, feature extraction methods, matching and decision algorithm. Out of the considered journals the most used is CASIA database at 40%, CCD Sensor camera with 56%, Gabor feature extraction method at 44%, Hamming distance for matching at 100% and PCA at 100% was used for decision making. These findings will assist the researcher in providing a guide on the best suitable components. Various researchers have proposed an improvement in the current security systems due to integrity and security problems.

Next2You: Robust Copresence Detection Based on Channel State Information

Context-based copresence detection schemes are a necessary prerequisite to building secure and usable authentication systems in theInternet of Things (IoT). Such schemes allow one device to verify proximity of another device without user assistance utilizing their physical context (e.g., audio). The state-of-the-art copresence detection schemes suffer from two major limitations: (1) They cannot accurately detect copresence in low-entropy context (e.g., empty room with few events occurring) and insufficiently separated environments (e.g., adjacent rooms), (2) They require devices to have common sensors (e.g., microphones) to capture context, making them impractical on devices with heterogeneous sensors. We address these limitations, proposingNext2You, a novel copresence detection scheme utilizing channel state information (CSI). In particular, we leverage magnitude and phase values from a range of subcarriers specifying a Wi-Fi channel to capture a robust wireless context created when devices communicate. We implementNext2Youon off-the-shelf smartphones relying only on ubiquitous Wi-Fi chipsets and evaluate it based on over 95 hours of CSI measurements that we collect in five real-world scenarios.Next2Youachieves error rates below 4%, maintaining accurate copresence detection both in low-entropy context and insufficiently separated environments. We also demonstrate the capability ofNext2Youto work reliably in real-time and its robustness to various attacks.

Algorithm Policy for the Authentication of Indirect Fingerprints Used in Cloud Computing

User identity identification secures cloud computing. This study examined cloud service security authentication needs. Fingerprint recognition was used to create a new cloud security authentication system. The proposed system's design and process were thoroughly examined to secure cloud user data from unauthorized access. This study proposes a secure cloud server fingerprint match technique. Considering fingerprint uniqueness and stability, cloud security login authentication technology employing fingerprint recognition is researched to improve cloud services login security. Analyze the cloud security login system structure first. Next, fingerprint identification is explained. Finally, fingerprint identification of cloud security login systems is investigated from fingerprint registration, certification, fingerprint image processing perspectives, and a simple fingerprint image processing simulation. The results show that this login mechanism is secure and versatile. The biometric template is insecure, and stolen templates cannot be canceled, making user identity leaks easy. This work proposes indirect fingerprint authentication to address these issues. Finally, a thorough security analysis of the cloud computing method is offered.

A Personalized Acoustic Interface for Wearable Human–Machine Interaction

AbstractCommunication and interaction with machines are changing our ways of life. However, developing an acoustic interface that simultaneously features waterproofness, wearability, high fidelity, and high accuracy for human–machine interaction remains a grand challenge. Herein, a waterproof acoustic sensor (WAS) as a wearable translation interface to communicate with machines is reported. Owing to the sound‐response ability of internal microparticles, the WAS holds a significantly broad frequency response range of 0.1–20 kHz, covering almost the entire human audible range. The WAS is stable against human perspiration, shows omnidirectional response, and displays an excellent frequency detection resolution of 0.0001 kHz. With a collection of compelling features, the WAS can serve as a wearable acoustic human–machine interface and a high‐fidelity auditory platform for music recording. Moreover, the WAS‐based acoustic interface holds a remarkable 98% accuracy for speech recognition with the assistance of an artificial intelligence algorithm. Finally, the WAS‐based acoustic interface demonstrates speaker verification and identification for implementation in highly secure biometric authentication systems and wireless control of an intelligent car using speech recognition. Such a WAS‐based acoustic interface represents the advancement of high‐fidelity translation platforms for human–machine interactions toward practical applications, including the Internet of Things, assistive technology, and intelligent recognition systems.

A Secure Biometric Key Generation Mechanism via Deep Learning and Its Application

Biometric keys are widely used in the digital identity system due to the inherent uniqueness of biometrics. However, existing biometric key generation methods may expose biometric data, which will cause users’ biometric traits to be permanently unavailable in the secure authentication system. To enhance its security and privacy, we propose a secure biometric key generation method based on deep learning in this paper. Firstly, to prevent the information leakage of biometric data, we utilize random binary codes to represent biometric data and adopt a deep learning model to establish the relationship between biometric data and random binary code for each user. Secondly, to protect the privacy and guarantee the revocability of the biometric key, we add a random permutation operation to shuffle the elements of binary code and update a new biometric key. Thirdly, to further enhance the reliability and security of the biometric key, we construct a fuzzy commitment module to generate the helper data without revealing any biometric information during enrollment. Three benchmark datasets including ORL, Extended YaleB, and CMU-PIE are used for evaluation. The experiment results show our scheme achieves a genuine accept rate (GAR) higher than the state-of-the-art methods at a 1% false accept rate (FAR), and meanwhile satisfies the properties of revocability and randomness of biometric keys. The security analyses show that our model can effectively resist information leakage, cross-matching, and other attacks. Moreover, the proposed model is applied to a data encryption scenario in our local computer, which takes less than 0.5 s to complete the whole encryption and decryption at different key lengths.

Chaotic Organic Crystal Phosphorescent Patterns for Physical Unclonable Functions.

Since the 4th Industrial Revolution, Internet of Things based environments have been widely used in various fields ranging from mobile to medical devices. Simultaneously, information leakage and hacking risks have also increased significantly, and secure authentication and security systems are constantly required. Physical unclonable functions (PUF) are in the spotlight as an alternative. Chaotic phosphorescent patterns are developed based on an organic crystal and atomic seed heterostructure for security labels with PUFs. Phosphorescent organic crystal patterns are formed on MoS2 . They seem similar on a macroscopic scale, whereas each organic crystal exhibits highly disorder features on the microscopic scale. In image analysis, an encoding capacity as a single PUF domain achieves more than 1017 on a MoS2 small fragment with lengths of 25µm. Therefore, security labels with phosphorescent PUFs can offer superior randomness and no-cloning codes, possibly becoming a promising security strategy for authentication processes.

Securing Logins in Electronic Examination Systems for Tertiary Institutions Using Quick Response Code (QR) Technology and Multiple Hashing Algorithms

This work is aimed at adding an extra layer of security to the login process of an electronic examination system as security has been identified as one of the critical success factors in the management of such exams. It proposes to secure the login process of an e-exam system through authentication and encryption to control access and avoid impersonation. A model of the e-exam system with Quick Response (QR) code generation capability was designed where a student’s matriculation number is accepted as input which is then converted into a two-dimensional bar code using a QR generator. Outputs from the QR code generator are then secured by encryption using MD5 and SHA-224 encryption algorithms. MD5 algorithm produces a 32-bit hash value which is further encrypted using SHA-224 that produces a resulting 56-bit hash value that is then saved in the password column of the user table in the database. This research resulted in a secure and web-based electronic examination authentication system implemented and tested on a client-server architecture. Performance evaluation of the developed system revealed that it is fast and effective, capable of authenticating students in an average of 0.624 seconds when the smartphone flashlight is off, and 0.318 seconds with flashlight turned on and consequently, resistant to brute force attacks. This paper fulfils an identified need to develop an electronic exam system that not only secures the question bank but equally ensures the security of the login process as well as the login details using a combination of two security techniques.

Real Time Face Recognition using Effective Supervised Machine Learning Algorithms

In security authentication systems, face recognition has become an emerging new trend. Modern FR processes can detect whether the individual is real (live) or not through face recognition, thus preventing the systems from being compromised by displaying an actual person’s image. These new systems of face recognition are the result of recent advances in the field of computer vision and efficient algorithms for machine learning. This thesis describes in depth how the identification of a real time face can be order to create a safety alert framework for the workplace, the machine learning algorithm Haarcascade classifier was used to build four distinct classes for identification of security equipment and eventually identify the faces in both images and video using python open CV. Initially, face detection technique is performed to scan the face identity marks through.

Design and Implementation of Cloud-Edge Integrated Security Authentication System

Design and Implementation of Cloud-Edge Integrated Security Authentication System