A novel cancellable biometric recognition system based on Rubik’s cube technique for cyber-security applications

A lightweight machine learning-based authentication framework for smart IoT devices

Most current security and authentication systems are based on personal biometrics. The security problem is a major issue in the field of biometric systems. This is due to the use in databases of the original biometrics. Then biometrics will forever be lost if these databases are attacked. Protecting privacy is the most important goal of cancelable biometrics. In order to protect privacy, therefore, cancelable biometrics should be non-invertible in such a way that no information can be inverted from the cancelable biometric templates stored in personal identification/verification databases. One methodology to achieve non-invertibility is the employment of non-invertible transforms. This work suggests an encryption process for cancellable speaker identification using a hybrid encryption system. This system includes the 3D Jigsaw transforms and Fractional Fourier Transform (FrFT). The proposed scheme is compared with the optical Double Random Phase Encoding (DRPE) encryption process. The evaluation of simulation results of cancellable biometrics shows that the algorithm proposed is secure, authoritative, and feasible. The encryption and cancelability effects are good and reveal good performance. Also, it introduces recommended security and robustness levels for its utilization for achieving efficient cancellable biometrics systems.

The Consultative Committee for Space Data Systems (CCSDS) recommends AES algorithm as a cryptographic algorithm to maintain confidentiality and data security between spacecraft and ground systems. In implementation, there are several AES algorithm operating modes including Cipher-Block Chaining (CBC), Electronic Codebook (EBC), Cipher Feedback (CFB), Output Feedback (OFB), and counter mode (CTR). One of the operating modes of AES algorithm recommended by CCSDS is CTR because it has advantages in terms of efficiency of implementation and offers a high level of data security. LAPAN-A4 as the next generation of LAPAN satellites also uses CCSDS standard in terms of data exchange between satellites and ground stations including telemetry and telecommand (TTC) data and satellite payload data. In order to protect LAPAN-A4 satellite data, especially TTC data, in this paper AES algorithm was implemented in CTR operating mode on the FPGA which will then be implemented on LAPAN-A4 satellite onboard data handling (OBDH) as one of LAPAN-A4 satellite data security systems. To overcome the limited resource of FPGA, optimization is also done using basic iterative AES hardware reused method, which is to reuse hardware that has been designed n times for same operation for AES algorithm. From the results of implementation and optimization, efficiency of logic gates used is increasing almost a quarter less than the previous research.

The need for fool proof authentication procedures away from traditional authentication mechanisms like passwords, security PINS has led to the advent of biometric authentication in information systems. Biometric data extracted from physiological features of a person including but not limited to fingerprints, palm prints, face or retina for purpose of verification & identification is saved as biometric templates. The inception of biometrics in access control systems has not been without its own hitches & like other systems it has its fair share of challenges. Biometric fingerprints being the most mature of all biometric spheres are the most widely adopted biometric authentication systems. Biometric systems effectiveness lies on how secure they are at preventing inadvertent disclosure of biometric templates in an information system’s archive. This however has not been the case as biometric templates have been fraudulently accessed to gain unauthorized access in identification and verification systems. In order to achieve strong and secure biometric systems, biometric systems developers need to build biometric systems that properly secure biometric templates. Several biometric template protection schemes and approaches have been proposed and used to safeguard stored biometric templates. Despite there being various biometric template protection schemes and approaches in existence, none of them has provided the most authentic, reliable, efficient and deterrent means to totally secure biometric fingerprint templates. This research sought to establish status of the current biometric template protection techniques and methods by conducting a survey and analyzing data gathered from a sample of seventy-eight (78) respondents. We will report these results and give our conclusion based on findings of the survey in this paper.

In this paper, we propose a method for the numerical simulation of optical double random phase encoding (DRPE). This method is based on a discrete model of the DRPE system that retains the properties of its optical counterpart and takes into account the capacity of DRPE to spread a signal’s energy in both the space and the spatial frequency domains. Discrete versions of two narrowband lossless diffusers are created to simulate their analogue optical counterparts and their bandwidths control how much the energy of the input signal is spread in both the space and spatial frequency domains as it passes through the system. The limiting case, when complete aliasing (maximum overlap) takes place, is also discussed in relation to our method. In this case, we show our method reduces to a purely numerical encryption procedure which can, nonetheless, be decrypted perfectly. Simulation results are presented to demonstrate the feasibility of the proposed technique.

In this paper, a proposed cancellable biometric scheme is based on multiple biometric image identifiers, Arnold’s cat map and double random phase encoding (DRPE) to obtain cancellable biometric templates. The proposed segmentation scheme that is used to select the region of interest for generating cancelable templates is based on chaos entropy low correlation statistical metrics. The objective of segmentation is to reduce the computational cost and reliability of template creation. The left and right biometric (iris, fingerprint, palm print and face) are divided into non-overlapping blocks of the same dimensions. To define the region of interest (ROI), we select the block with the highest entropy. To shorten the registration process time and achieve a high level of security, we select 25% of the image volume of the biometric data. In addition, the low-cost security requirement lies in the use of selective encryption (SE) technology. The step of selecting the maximum entropy is executed on all biometric blocks. The maximum right and left multi-biometric blocks are arranged in descending order from the entropy perspective and select 50% of each biometric couple and store the single matrix. The obtained matrix is scrambled with a certain number of iterations using Arnold’s Cat Map (ACM).The obtained scrambled matrix is encrypted with the DRPE to generate the cancellable biometric templates, which are further concatenated. The simulation results display better performance of the suggested cancellable biometric system in noise scenarios using the area under the receiver operating characteristic (AROC). The strength of the suggested technique is examined with correlation, irregular deviation, maximum difference and maximum deviation. The recommended proposed approach shows that the ability to distinguish the authentic and imposter biometrics of user seven in different levels of the noise environment.

Cancelable biometric techniques play a vital role in the privacy and security of remote surveillance systems to keep the genuine users’ confidential data safe and away from intruders. This research presents an efficient cancelable biometric framework by exploiting an irreversible hybrid encryption technique that incorporates Deoxyribonucleic, Ribonucleic acid sequences (DNA and RNA), and the evolutionary optimization technique of the Genetic Algorithm (GA). So, these techniques are employed to create completely deformed templates from their original ones. Thus, our main contribution is offering a novel biometric security framework that achieves unique randomness characteristics using RNA and DNA sequences and the evolutionary GA-based encryption scheme. The proposed framework produces an entirely unrecognized biometric template by ciphering the main discriminative features of the biometric traits of the authorized clients. The proposed algorithm is first initialized by creating several encrypted biometric images of the original users by employing the logistic map function. Secondly, the initialized encrypted images are transformed into one vector of a binary array. Then, they are converted to their corresponding introns, exons, and consequently, their relevant codons are stored in the cloud database. Thirdly, these relevant codons are replaced by new ones after generating encrypted RNA lists. The utilized encryption key is extracted from the original biometric image after utilizing excessive permutations between its pixels. Then, the GA-based optimization technique is applied to select the most convenient biometric features. Finally, after employing the GA-based cross-over and mutation operations, the chosen features are used to generate the cancelable biometric traits. To validate the security efficacy of the proposed framework, different six biometric databases are used, which are Olivetti Research Laboratory (ORL) Faces (gray), CASIA v.5 Faces (color), UPOL Iris (gray), Indian Institute of Technology Delhi (IIT Delhi) Ear (color and gray), Fingerprint, and CASIA Palmprint (color and gray). The security performance of the proposed framework is compared to those of recent studies in this field, such as OSH (Optical Scanning Holography) and DRPE (Double Random Phase Encoding). The simulation results prove the superior performance of the proposed framework in terms of all tested evaluation metrics. The proposed framework provides high AROC (Area under the Receiver Operating Characteristic) that reaches 0.9990, low FAR (False Acceptance Rate) of 0.0015, more uniform histograms, high correlation values for genuine users, and complete hidden of biometric features. In addition, from a security perspective, the proposed framework accomplishes good entropy, UACI (unified average changing intensity), and NPCR (number of pixels change rate) values that reach 7.9960, 33.55%, and 99.65%, respectively.

Background: With the growing need for information safety and security rules in every corner of the globe. The biometric innovation has been used widely all over the world in daily life. In this view, the Multimodal-based biometric technique has gained popularity and interest due to its capability to resolve various problems associated with single-model biometric identification systems Methods:: In this research, an advanced multimodal-based biometric recognition system is introduced, which depends on multilayer perceptron (MLP), and it identifies humans using biometric features of Fingerprints and IRIS. To build an efficient model, the popular model called Rest- Net50 was used, the gradient method was used and the hinge technique was used as a loss function. For the technique of fusion, different techniques are used to show the effect of fusion in our model. The recognition rate of introduced systems was assessed by experimenting with various techniques on SDUMLA-HMT datasets based on the multimodal biometric datasets. The recognition rate showed that merging two biometric features in biometric recognition systems gives the best results compared to a single biometric feature. Results: The results obtained showed the superiority of our model with other existing techniques by obtaining a recognition rate of 99.79% with a feature-based fusion method. Conclusion: The introduced system used the MLP deep learning technique. The feature level fusion was applied to recognize the user using IRIS and fingerprints. To the best of our belief, this is the first research to incorporate MLP and multimodal-based biometric systems using IRIS and fingerprints.

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.

Traditional biometric systems are designed and configured to operate in predefined circumstances to address the needs of a particular application. The performance of such biometrics systems tend to decrease because when they encounter varying conditions as they are unable to adapt to such variations. Many real-life scenarios require identification systems to recognise uncooperative people in uncontrolled environments. Therefore, there is a real need to design biometric systems that are aware of their context and be able to adapt to changing conditions. The context-awareness and adaptation of a biometric system are based on a set of factors that include: the application (e.g. healthcare system, border control, unlock smart devices), environment (e.g. quiet/noisy, indoor/outdoor), desired and pre-defined requirements (e.g. speed, usability, reliability, accuracy, robustness to high/low quality samples), user of the system (e.g. cooperative or non-cooperative), the chosen modality (e.g. face, speech, gesture signature), and used techniques (e.g. pre-processing to normalise and clean biometrics data, feature extraction and classification). These factors are linked and might affect each other, hence the system has to work adaptively to meet its overall aim based to its operational context. The aim of this research is to develop a multiagent based framework to represent a context-aware adaptive biometric system. This is to improve the decision making process at each processing step of traditional biometric identification systems. Agents will be used to provide the system with intelligence, adaptation, flexibility, automation, and reliability during the identification process. The framework will accommodate at least five agents, one for each of the five main processing steps of a typical biometric system (i.e. data capture, pre-processing, feature extraction, classification and decision). Each agent can contribute differently towards its designated goal to achieve the best possible solution by selecting/ applying the best technique. For example, an agent can be used to assess the quality of the input biometric sample to ensure the important features can be extracted and processed in further steps. Another agent can be used to pre-process the biometric sample if necessary. A third agent is used to select the appropriate set of features followed by another to select a suitable classifier that works well in a given condition.

Score normalization applied to adaptive biometric systems

Cancelable biometric authentication system based on hyperchaotic technique and fibonacci Q-Matrix

In recent years, the speaker recognition technology is significant system for securing the biometric authentication and enabling remote logins for telephone banking and enterprise applications. Whereas Speech Signal Detection ensure with accurate speaker identification and verification for restricted access and sensitive data protection. The combination of speaker recognition technology with speech signal detection has accompanied in a new era of biometric authentication, providing unparalleled security and accuracy in verifying identities. However, Vulnerability to voice spoofing attacks leads to imitation of speaker such as mimicry or synthetic speech, compromise system security. To overcome these drawbacks, an Optical Scanning Holography with Double Random Phase Encoding (OSH-DRPE) is proposed using Texas Instruments Massachusetts Institute of Technology (TIMIT) dataset. After collecting the data, the data is preprocessed through normalization approaches for normalize the data. Then, a Mel-Frequency Cepstral Coefficients (MFCC) is employed for feature extraction to extract relevant features, which produces low computational complexity. At last, OSH-DRPE is used as classifier to detect speech signal and speaker recognition with higher accuracy, efficient computation and flexibility. From the results, it clearly shows that the proposed OSH-DRPE obtained higher values in the context of precision, F1 score and accuracy when compared to existing Hidden Markov Model (HMM).

In a biometric authentication system using protected templates, a pseudonymous identifier is the part of a protected template that can be directly compared. Each compared pair of pseudonymous identifiers results in a decision testing whether both identifiers are derived from the same biometric characteristic. Compared to an unprotected system, most existing biometric template protection methods cause to a certain extent degradation in biometric performance. Fusion is therefore a promising way to enhance the biometric performance in template-protected biometric systems. Compared to feature level fusion and score level fusion, decision level fusion has not only the least fusion complexity, but also the maximum interoperability across different biometric features, template protection and recognition algorithms, templates formats, and comparison score rules. However, performance improvement via decision level fusion is not obvious. It is influenced by both the dependency and the performance gap among the conducted tests for fusion. We investigate in this paper several fusion scenarios (multi-sample, multi-instance, multi-sensor, multi-algorithm, and their combinations) on the binary decision level, and evaluate their biometric performance and fusion efficiency on a multi-sensor fingerprint database with 71,994 samples.

Background: Among optical encryption techniques, Double Random Phase Encoding (DRPE) is one of the most widely used. Individual identities and the process of recognition remain essential to ensuring proper data access security.Aim: The study aims to optimize an approach that ensures the significant performance effectiveness of the cancelable biometric methods for different templates and the associated time taken to transform biometric data.Problem: This study is majorly concerned about the performance effectiveness of cancelable biometric methods that measure the likelihood that an authorized effort may be mistakenly rejected as unauthorized. Also, when compromised, several non-renewability safety challenges arise, and insufficient matching performance templates are required to build a security protection method.Method and material. The study uses supervised learning for the Double Random Phase Encoding Method (DRPE), a 4F optical encryption system, and 20 randomly chosen photos from the ORL database of faces.Results. The result based on the supervised learning for the Double Random Phase Encoding Method revealed false positive rates for both the fingerprint and face templates.Conclusion. The study concluded that the performance effectiveness of the cancelable biometric in this study has a false positive rate likelihood that an authorized effort may not be mistakenly rejected as an unauthorized one.