Secure access microcontroller system based on fingerprint template with hyperchaotic encryption

Security issues have become a major issue on the Internet. One of the security methods that are widely used today is to implement a digital certificate. Digital certificates have evolved over time, one of which is the X.509 digital certificate. Digital certificates have been widely used as authentication applications, web network authentication and other authentication systems that require digital certificates. This research is carried out by implementing an X.509 digital certificate technology as a mobile web service with its client. Secure Hash Algorithm (SHA), Diffie-Hellman, and Advanced Encryption Standard (AES) are used to secure the data exchange transaction between the web service and mobile phone. SHA algorithm will be used for user authentication, Diffie-Hellman algorithm will be used for public key exchange and AES algorithms will be used for symmetric cryptography data. The results of the application of digital certificates, the SHA algorithm, Diffie-Hellman, and AES in mobile phone applications, provide security application running on web service. Index Terms”Digital Certificate, X.509, SHA, Diffie Hellman, AES

A linear convolution-based cancelable fingerprint biometric authentication system

In medical systems for patient's authentication, keeping biometric data secure is a general problem. Many studies have presented various ways of protecting biometric data especially finger vein biometric data. Thus, It is needs to find better ways of securing this data by applying the three principles of information security aforementioned, and creating a robust verification system with high levels of reliability, privacy and security. Moreover, it is very difficult to replace biometric information and any leakage of biometrics information leads to earnest risks for example replay attacks using the robbed biometric data. In this paper presented criticism and analysis to all attempts as revealed in the literature review and discussion the proposes a novel verification secure framework based confidentiality, integrity and availability (CIA) standard in triplex blockchain-particle swarm optimization (PSO)-advanced encryption standard (AES) techniques for medical systems patient's authentication. Three stages are performed on discussion. Firstly, proposes a new hybrid model pattern in order to increase the randomization based on radio frequency identification (RFID) and finger vein biometrics. To achieve this, proposed a new merge algorithm to combine the RFID features and finger vein features in one hybrid and random pattern. Secondly, how the propose verification secure framework are followed the CIA standard for telemedicine authentication by combination of AES encryption technique, blockchain and PSO in steganography technique based on proposed pattern model. Finally, discussed the validation and evaluation of the proposed verification secure framework.

Template-Protection-Techniken stellen eine wichtige Ergänzung zu biometrischen Systemen, um das gewünschte Sicherheitsniveau zu erreichen und die Anforderungen an den Schutz der Privatsphäre zu erfüllen. Diese Techniken ermöglichen Erneuerung und Widerruf der Referenzendaten in biometrischen Systemen. In dieser Arbeit wird ein systematisches Evaluierungsframework entwickelt, das sich an die wesentlichen Kriterien und Anforderungen an biometrische Template-Protection-Verfahren festhält. Seine Anwendbarkeit wird durch die Analyse der verschiedenen Algorithmen demonstriert. Die in dieser Arbeit präsentierte Evaluierung ist grundlegend für eine vollständige Analyse. Darüber hinaus ermöglicht es einen Nachweis der Sicherheit und Einhaltung der Privatsphäre. Es schafft eine Basis für Zertifizierung und Benchmark der Template-Protection-Verfahren.

This paper proposes a green cloud multi-tenant trust authentication with secure hash algorithm-3 (GreenCloud-MTASHA3) scheme to eliminate the unauthorised tenant access. GreenCloud-MTASHA3 scheme provide security over the multiple tenant requests by referring the confidentiality, integrity and availability rate. Confidentiality refers to limiting the unauthorised tenant's green cloud data access using the additive homomorphic privacy property in proposed scheme. Additive homomorphic privacy property-based encryption function is developed to improve the privacy preserving level. To attain the integrity level between the tenant requests and green cloud server machine in GreenCloud-MTASHA3 scheme an encrypted trust data management process is carried out. Trustworthiness of tenant request is measured to maintain the consistency level on security with minimal computational time. The proposed scheme attains the confidentiality, integrity and availability rate on communicating task. Experiment is conducted on factors such as secure computation confidence, authorised tenant computational time and space taken on storing encrypted data.

This paper proposes a green cloud multi-tenant trust authentication with secure hash algorithm-3 (GreenCloud-MTASHA3) scheme to eliminate the unauthorised tenant access. GreenCloud-MTASHA3 scheme provide security over the multiple tenant requests by referring the confidentiality, integrity and availability rate. Confidentiality refers to limiting the unauthorised tenant's green cloud data access using the additive homomorphic privacy property in proposed scheme. Additive homomorphic privacy property-based encryption function is developed to improve the privacy preserving level. To attain the integrity level between the tenant requests and green cloud server machine in GreenCloud-MTASHA3 scheme an encrypted trust data management process is carried out. Trustworthiness of tenant request is measured to maintain the consistency level on security with minimal computational time. The proposed scheme attains the confidentiality, integrity and availability rate on communicating task. Experiment is conducted on factors such as secure computation confidence, authorised tenant computational time and space taken on storing encrypted data.

A robust embedded biometric authentication system based on fingerprint and chaotic encryption

Access control and security within higher education institutions are of paramount importance in safeguarding sensitive information and resources. Conventional authentication methods, such as passwords and identification cards, have proven susceptible to security breaches and identity theft. To address these vulnerabilities, this paper presents a novel biometric authentication scheme tailored to the unique needs of higher education institutions. Deploying a trustworthy user authentication system became a key responsibility for both access control and securing user's private data with the rapid rise of electronic crimes and their connected difficulties. For both private and public use, human biometric features including voice, finger, iris scanning, face, signature, and other features offer a solid security level. This paper provides a comprehensive overview of the biometric authentication scheme, outlining its architecture, functionality, and security measures. We also present the results of a pilot implementation within a higher education institution, demonstrating improved access security and user satisfaction. Ethical considerations and privacy safeguards are discussed to ensure responsible biometric data handling. For a long time, numerous biometric authentication solutions have been considered. owing to the distinctiveness of human biometrics, which was important in thwarting imposters' attacks. Only a few of the key issues endangering system integrity and impeding effective service delivery include identity theft, spoofing, and the reliability of authentication systems in higher education institutions. From the experiment the total number of tests was 15, as the threshold was one attempt. While fingerprint authentication typically took 2.67 seconds, palm vein authentication often took 9.15 seconds. Therefore, the palm vein was slower than the fingerprint in terms of speed. The structure of the hand and the distance between the palm and the scanner were the determining elements in pal's slow authentication speed. The palm vein system has a 93.33% accuracy rate compared to the fingerprint system's 60% accuracy rate, making it the preferable model to use in a higher education setting. A biometric system's success or failure is influenced by a variety of variables and application domains. The purpose of this work is to discuss an appropriate biometric authentication model that may be used to improve the reliability of biometric systems in institutions of higher learning. The proposed biometric authentication scheme offers a forward-looking solution to the access integrity challenges faced by higher education institutions. By adopting this technology, institutions can bolster their security posture, protect sensitive data, and provide a more convenient and secure access experience for students, faculty, and staff.

Biometric data, such as fingerprints, facial recognition, retina scans, and voice scans, constitute unique and irreplaceable forms of personal identification. In the digital era, biometric data is increasingly used in electronic security systems for identity verification and user authentication. However, despite these benefits, there are potential risks of privacy violations and data misuse, which impact human rights. This study aims to analyze the legal protection of biometric data in digital security systems from a criminal law perspective and examine criminal liability in the event of biometric data leaks or misuse. The research method used is a normative juridical approach, examining relevant statutory provisions, such as Law Number 27 of 2022 concerning Personal Data Protection, Law Number 11 of 2008 concerning Electronic Information and Transactions (ITE), and the Criminal Code (KUHP). The results show that although biometric data has been recognized as legally protected personal data, criminal law enforcement mechanisms for biometric data-related violations are still suboptimal. This is due to the lack of clear technical regulations and limitations regarding the definition, storage, and use of biometric data by electronic system administrators. This study concludes that strengthening criminal law regulations and implementing policies is necessary to ensure comprehensive biometric data protection, including imposing strict sanctions on perpetrators of misuse and increasing corporate responsibility in maintaining the security of the public’s digital data.

A lot of attempts have been made to use biometrics in class attendance systems. Most of the implemented biometrie attendance systems are unimodal. Unimodal biometrie systems may be spoofed easily, leading to a reduction in recognition accuracy. This paper explores the use of bimodal biometrics to improve the recognition accuracy of automated student attendance systems. The system uses the face and fingerprint to take students' attendance. The students' faces were captured using webcam and preprocessed by converting the color images to grey scale images. The grey scale images were then normalized to reduce noise. Principal Component Analysis (PCA) algorithm was used for facial feature extraction while Support Vector Machine (SVM) was used for classification. Fingerprints were captured using a fingerprint reader. A thinning algorithm digitized and extracted the minutiae from the scanned fingerprints. The logical technique (OR) was used to fuse the two biometric data at the decision level. The fingerprint templates and facial images of each user were stored along with their particulars in a database. The implemented system had a minimum recognition accuracy of 87.83%.

Most of the fingerprint recognition systems depend on pair-wise matching of the minutia feature vectors which consist of several spatial properties associated with the minutia points. Due to various non-linear deformations like translation and rotation, minutia based matching is quite a challenging issue. To deal with this problem, most of the techniques in the literature solely depend upon a reference point selection. In this paper, we propose a novel cluster based template generation and matching algorithm which is invariant to translation and rotation of the minutia points. The cardinality of minutia clusters are stored as fingerprint template instead of minutia feature vectors. The stored template reveals no information regarding the spatial pattern of the fingerprint which provides a strong security against information leakage. Finally, a robust match score estimation technique is introduced to increase the matching efficiency. Moreover, the proposed method produces a faster execution. We present the performance on FVC2004 database to show the effectiveness of our method along with a comparative study against some other existing matching algorithms which establishes the efficacy of the proposed method.

The article discusses the problem of ensuring proper protection of an individual’s digital data. Interacting online with other users, everyone leaves a digital footprint in one way or another, while forming an image of a digital personality. To gain access to various online services, you need credentials, financial information, and biometric data. The totality of such information makes it possible to identify a person in a digital environment and constitutes his digital identity. This kind of data can be the subject of digital identity theft. In addition to obtaining data about individuals, identity theft includes their use or sale, transfer to others, and further use in illegal activities. Artificial intelligence technologies have become one of the new methods used for identity theft and fraud. Deepfake technology can be used to steal personal data, just as digital identity data can be used to create a deepfake. The article concludes that in most cases the norms of the current criminal legislation can be applied to qualify the described acts. The need to focus efforts to counter identity theft and the use of deepfake technologies on a set of measures is emphasized.

Biometrics are increasingly used for access control, fraud detection, and authentication systems. Nevertheless, attackers can deceive such systems using forged biometrics. This research proposes a novel method that makes biometric security systems more resilient to such attacks. The proposed method transforms the user’s biometric data into an irreversible code to protect the original data. This code combines data from multiple biometric modalities, making fabricating a false biometric harder. Additionally, the proposed method does not depend on any secret keys, which helps avoid cases of stolen tokens. The proposed method utilizes the generative adversarial network (GAN) to generate synthetic biometric templates from multiple modalities, which is considered a transformation function for biometric data. Three fusion levels are presented; features from multiple biometric modalities are extracted first in each fusion level. Subsequently, the features train a generative adversarial network to produce synthesized biometric templates. These synthesized templates serve as secure substitutes for the original biometrics during authentication, preventing direct exposure of raw biometric data. We evaluated our methods on the CASIA-V3-Internal and MMU1 iris datasets and the AT&T (ORL) and FERET face datasets. The results showed that our proposed methods can achieve higher accuracy, usability, and improved security compared to a single biometric modality. The proposed feature-level, GAN-based, and decision-level fusion schemes achieved 2.03%, 0.82%, and 0.0297% error rates, respectively, for CASIA and ORL datasets and 1.53%, 0.80%, and 0.0313% error rates, respectively, for MMU1 and FERET datasets. Moreover, we have demonstrated that our method resists pre-image and correlation attacks.

We propose a content-based semi-fragile watermarking algorithm for image authentication. In content-based watermarking scheme for authentication, one of the most challenging issues is to define a computable feature vector that can capture the major content characteristics. We identify Zernike moments of the image to generate feature vector and demonstrate its good robustness and discriminative capability for authentication. The watermark is generated by quantizing Zernikemoments magnitudes (ZMMs) of the image and embedded into DWT (Discrete Wavelet Transform) subband. It is usually hard to locate the tampered area by using global feature in the content-based watermarking scheme. We propose a structural embedding method to locate the tampered areas by using the separability of Zernike moments-based feature vector. The authentication process does not need the original feature vector. By using the semi-fragilities of the feature vector and the watermark, the proposed authentication scheme is robust to content-preserved processing, while being fragile to malicious attacks. As an application of our algorithm, we apply it on Chinese digital seals and the results show that it works well. Compared with some existing algorithms, the proposed scheme achieves better performance in discriminating high-quality JPEG compression from malicious attacks.

With technological advancements and the increasing use of computers and internet in our day to day lives, the issue of security has become paramount. The rate of cybercrime has increased tremendously in the internet era. Out of the numerous crimes, identity theft is perhaps the one that poses the most dangers to an individual. More and more voices strongly declare that the password is no longer a reliable IT security measure and must be replaced by more efficient systems for protecting the computer contents. Behavioural biometrics is an emerging technology that resolves some of the major flaws of the previous scheme. This paper is the first stage of a project which aims to develop a novel authentication system using behavioural biometrics. It presents a comprehensive survey of various techniques and recent works in the respective fields.