Secure Multimodal Authentication Scheme for Wireless Sensor Networks

In the current era, it is necessary to device authorization and authentication techniques to secure resources in information technology. There are several methods to substantiate authorization and authentication. User authentication is essential for authenticating user access control in WSNs. Biometric recognition error, lack of anonymity and vulnerability to attacks, user verification problem, revocation problem and disclosure of session key by the gateway node are some of the security flaws encountered. In this study, a Multimodal Authentication Scheme for Wireless Sensor Networks (WSN-MAS) is proposed to authenticate legitimate users. The main objective is the fusion of fingerprint and iris biometric features at feature level to enable additional accuracy to verify and match user identity with stored templates. In this paper, multimodal biometric features are used for authentication to improve performance, reduce system error rates to achieve better security in WSN.

Evaluation of ocular biometric parameters in keratoconic eyes relative to healthy myopic eyes

Objective To assess the biometric features of keratoconic eyes using the Lenstar LS900 and Pentacam systems relative to healthy myopic eyes. Materials and Methods Seventy-three eyes of keratoconic subjects and 83 eyes of control subjects were enrolled. To evaluate the reproducibility of the Lenstar and Pentacam devices’ measurements, keratometric readings [in flattest meridian (Kf), in steepest meridian (Ks), and mean (Km)], central corneal thickness (CCT), and anterior chamber depth (ACD) were obtained using both systems. Axial length and lens thickness (LT) were measured by the Lenstar. The compatibility between the two devices was investigated using the Bland-Altman statistical method. Results Axial length was longer in the myopic group than in eyes with keratoconus (24.94 ± 0.7 and 23.88 ± 0.96 mm, respectively, p < 0.001). LT and vitreous depth were also higher in the myopic group, although ACD values were similar. Compared to the Lenstar, the Pentacam measured the ACD and CCT values higher in the myopia group [with a difference of 0.07 ± 0.12 mm ( p <0.001) and 4.47 ± 11.33 µm ( p = 0.001), respectively] and measured the CCT values higher in the keratoconus group. Pentacam found all keratometry values significantly lower than Lenstar in the keratoconus group. Conclusions Axial length was longer in the myopic eyes due to the differences starting from the lens and extending to the posterior segment. Lenstar and Pentacam can be used interchangeably for Km, Kf, and ACD in the myopic group and only for ACD in the keratoconus group.

Multimodal Identification Based on Fingerprint and Face Images via a Hetero-Associative Memory Method

Multimodal identification, which exploits biometric information from more than one biometric modality, is more secure and reliable than unimodal identification. Face recognition and fingerprint recognition have received a lot of attention in recent years for their unique advantages. However, how to integrate these two modalities and develop an effective multimodal identification system are still challenging problems. Hetero-associative memory (HAM) models store some patterns that can be reliably retrieved from other patterns in a robust way. Therefore, in this paper, face and fingerprint biometric features are integrated by the use of a hetero-associative memory method for multimodal identification. The proposed multimodal identification system can integrate face and fingerprint biometric features at feature level when the system converges to the state of asymptotic stability. In experiment 1, the predicted fingerprint by inputting an authorized user’s face is compared with the real fingerprint, and the matching rate of each group is higher than the given threshold. In experiment 2 and experiment 3, the predicted fingerprint by inputting the face of an unauthorized user and the stealing authorized user’s face is compared with its real fingerprint input, respectively, and the matching rate of each group is lower than the given threshold. The experimental results prove the feasibility of the proposed multimodal identification system.

Cancelable Multimodal Biometrics Based on Chaotic Maps

Biometric authentication systems raise certain concerns with regard to security, violation of privacy, and storage issues of biometric templates. This paper proposes a protection approach of biometric templates storage in a multimodal biometric system while ensuring both the cancelability of biometric templates and the efficiency of the authentication process. We propose applying a chaotic maps-based transform on the biometric features to address the cancelability issue. We used Logistic map and Torus Automorphism to generate cancelable biometric features of the face and fingerprint minutia points, respectively. Both transformed features would be concatenated and saved in the database of the system instead of the original features. In the authentication stage, the similarity scores of both transformed face and fingerprint templates are computed and fused using the weighted sum rule. The results of the experimentation, conducted using images from the ORL face and FVC2002 DB1 fingerprint databases, demonstrated the higher performance of the proposed approach achieving a genuine accept rate equal to 100%. Moreover, the obtained results confirmed the soundness of the proposed cancelable technique to satisfy the biometric systems’ requirements (i.e., security, revocability, and diversity).

Secure Online Examination with Biometric Authentication and Blockchain-Based Framework

E-learning has been carried out all over the world and then online examinations have become an important means to check learning effect during the outbreak of COVID-19. Participant authenticity, data integrity, and access control are the assurance to online examination. The existing online examination schemes cannot provide the protection of biometric features and fine-grained access control. Particularly, they did not discuss how to resolve some disputes among students, teachers, and a platform in a fair and reasonable way. We propose a novel biometric authentication and blockchain-based online examination scheme. The examination data are encrypted to store in a distributed system, which can be obtained only if the user satisfies decryption policy. And the pieces of evidence are recorded in a blockchain network which is jointly established by some credible institutions. Unlike other examination authentication systems, face templates in our scheme are protected using a fuzzy vault and a cryptographic method. Furthermore, educational administrative department can determine who the real initiator of malicious behavior is when a dispute arises using a dispute determination protocol. Analysis shows that no central authority is required in our scheme; the collusion of multiple users cannot obtain more data; even if the authorities compromise, biometric features of each user will not be leaked. Therefore, in terms of privacy-preserving biometric templates, fine-grained access, and dispute resolution, it is superior to the existing schemes.

Enhancing Security in Internet of Things Environment by Developing an Authentication Mechanism using COAP Protocol

Internet of Things (IoT) has a huge attention recently due to its new emergence, benefits, and contribution to improving the quality of human lives. Securing IoT poses an open area of research, as it is the base of allowing people to use the technology and embrace this development in their daily activities. Authentication is one of the influencing security element of Information Assurance (IA), which includes confidentiality, integrity, and availability, non repudiation, and authentication. Therefore, there is a need to enhance security in the current authentication mechanisms. In this report, some of the authentication mechanisms proposed in recent years have been presented and reviewed. Specifically, the study focuses on enhancement of security in CoAP protocol due to its relevance to the characteristics of IoT devices and its need to enhance its security by using the symmetric key with biometric features in the authentication. This study will help in providing secure authentication technology for IoT data, device, and users.

Statistical-Shape Prediction of Lower Limb Kinematics During Cycling, Squatting, Lunging, and Stepping—Are Bone Geometry Predictors Helpful?

Purpose: Statistical shape methods have proven to be useful tools in providing statistical predications of several clinical and biomechanical features as to analyze and describe the possible link with them. In the present study, we aimed to explore and quantify the relationship between biometric features derived from imaging data and model-derived kinematics.Methods: Fifty-seven healthy males were gathered under strict exclusion criteria to ensure a sample representative of normal physiological conditions. MRI-based bone geometry was established and subject-specific musculoskeletal simulations in the Anybody Modeling System enabled us to derive personalized kinematics. Kinematic and shape findings were parameterized using principal component analysis. Partial least squares regression and canonical correlation analysis were then performed with the goal of predicting motion and exploring the possible association, respectively, with the given bone geometry. The relationship of hip flexion, abduction, and rotation, knee flexion, and ankle flexion with a subset of biometric features (age, length, and weight) was also investigated.Results: In the statistical kinematic models, mean accuracy errors ranged from 1.60° (race cycling) up to 3.10° (lunge). When imposing averaged kinematic waveforms, the reconstruction errors varied between 4.59° (step up) and 6.61° (lunge). A weak, yet clinical irrelevant, correlation between the modes describing bone geometry and kinematics was observed. Partial least square regression led to a minimal error reduction up to 0.42° compared to imposing gender-specific reference curves. The relationship between motion and the subject characteristics was even less pronounced with an error reduction up to 0.21°.Conclusion: The contribution of bone shape to model-derived joint kinematics appears to be relatively small and lack in clinical relevance.

A State of Art for Survey of Combined Iris and Fingerprint Recognition Systems

Biometrics is developing into a technological science in this lifelong technology for the defense of identification. Biometrics is the technology to recognize individuals based on facial features, fingerprints, iris, retina, speech, handprints, etc. Biometric features are used for human recognition and identification. Much research was done in the last years on the biometric system because of a growing need for identification methods. This paper offers an overview of biometric solutions using fingerprint and iris identification, their uses, and Compare the data set, methods, Fusion Level, and the accuracy of the results.