The technique of selecting a guard band in the climatic tests of radio-electronic equipment

The purpose of this study is to create a methodology for assessing the risks arising from climatic tests of radio-electronic equipment. The article considers the effect of additional misalignment of transmission lines arising under conditions of temperature inhomogeneities on the increase in consumer and producer risks. A method of selecting a protective measuring band is proposed to minimize the probability of false acceptance and rejection of the tested devices. The results obtained make it possible to determine the optimal, from the point of view of the risks of the consumer and the manufacturer, the coefficient of the protective band of radio engineering measurement.

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Electronic documents constitute specific units of information, and protecting them against unauthorized access is a challenging task. This is because a password protected document may be stolen from its host computer or intercepted while on transfer and exposed to unlimited offline attacks. The key issue is, therefore, making document passwords hard to crack. We propose to augment a common text password authentication interface to encrypted documents with a biometric facial identity verification providing highly personalized security mechanism based on pseudo-identities. In consequence the encrypted document can be unlocked with the legitimate user’s face, while for everyone else stays encrypted with a hard to crack text password. This paper makes two contributions: (1) The proposed scheme enables password autofill without referring to any external service, which significantly limits the possibilities of an attack by adversaries when opening, reading and editing the protected document, (2) By the adoption of biometric verification techniques enabling fine-tuning of false acceptance and false rejection rates, it provides for responsible adaptation to users.

Efficiently Updating ECG-Based Biometric Authentication Based on Incremental Learning

Recently, the interest in biometric authentication based on electrocardiograms (ECGs) has increased. Nevertheless, the ECG signal of a person may vary according to factors such as the emotional or physical state, thus hindering authentication. We propose an adaptive ECG-based authentication method that performs incremental learning to identify ECG signals from a subject under a variety of measurement conditions. An incremental support vector machine (SVM) is adopted for authentication implementing incremental learning. We collected ECG signals from 11 subjects during 10 min over six days and used the data from days 1 to 5 for incremental learning, and those from day 6 for testing. The authentication results show that the proposed system consistently reduces the false acceptance rate from 6.49% to 4.39% and increases the true acceptance rate from 61.32% to 87.61% per single ECG wave after incremental learning using data from the five days. In addition, the authentication results tested using data obtained a day after the latest training show the false acceptance rate being within reliable range (3.5–5.33%) and improvement of the true acceptance rate (70.05–87.61%) over five days.

Robustness of Rhythmic-Based Dynamic Hand Gesture with Surface Electromyography (sEMG) for Authentication

Authentication has three basic factors—knowledge, ownership, and inherence. Biometrics is considered as the inherence factor and is widely used for authentication due to its conveniences. Biometrics consists of static biometrics (physical characteristics) and dynamic biometrics (behavioral). There is a trade-off between robustness and security. Static biometrics, such as fingerprint and face recognition, are often reliable as they are known to be more robust, but once stolen, it is difficult to reset. On the other hand, dynamic biometrics are usually considered to be more secure due to the constant changes in behavior but at the cost of robustness. In this paper, we proposed a multi-factor authentication—rhythmic-based dynamic hand gesture, where the rhythmic pattern is the knowledge factor and the gesture behavior is the inherence factor, and we evaluate the robustness of the proposed method. Our proposal can be easily applied with other input methods because rhythmic pattern can be observed, such as during typing. It is also expected to improve the robustness of the gesture behavior as the rhythmic pattern acts as a symbolic cue for the gesture. The results shown that our method is able to authenticate a genuine user at the highest accuracy of 0.9301 ± 0.0280 and, also, when being mimicked by impostors, the false acceptance rate (FAR) is as low as 0.1038 ± 0.0179.

Palmprint False Acceptance Attack with a Generative Adversarial Network (GAN)

Biometric-based authentication is widely deployed on multimedia systems currently; however, biometric systems are vulnerable to image-level attacks for impersonation. Reconstruction attack (RA) and presentation attack (PA) are two typical instances for image-level attacks. In RA, the reconstructed images often have insufficient naturalness due to the presence of remarkable counterfeit appearance, thus their forgeries can be easily detected by machine or human. The PA requires genuine users’ original images, which are difficult to acquire in practice and to counterfeit fake biometric images on spoofing carriers. In this paper, we develop false acceptance attack (FAA) for a palmprint biometric, which overcomes the aforementioned problems of RA and PA. FAA does not require genuine users’ images, and it can be launched simply with the synthetic images with high naturalness, which are generated by the generative adversarial networks. As a case study, we demonstrate the feasibility of FAA against coding-based palmprint biometric systems. To further improve the efficiency of FAA, we employ a clustering method to select diverse fake images in order to enhance the diversity of the fake images used, so the number of attack times is reduced. Our experimental results show the success rate and effectiveness of the FAA.

Real Time Implementation of Voice Activity Detection based on False Acceptance Regulation

In speech compression systems, Voice Activity Detection (VAD) is frequently used to distinguish active voice from other noisy sounds. In this paper, a robust approach of VAD is presented to deal with non-stationary noisy environments. The proposed algorithm exploits adaptive thresholding technique to keep a desired False Acceptance (FA) rate. Iterative hypothesis tests, using signal energy, are implemented to discard or to accept the successive audio frames as active voice. According to the stationary property of the speech, we provide a smoothing method to obtain final VAD decisions. The main contribution of the proposed algorithm concerns its ability to automatically adjust the energy threshold according to the local noise estimator. We analyzed the proposed approach by presenting a comparison with the G.729-B via the NOIZEUS database. The VAD architecture is implemented on a Microcontroller-based system (MCU). Several tests have been conducted by performing real time acquisition via the Input/Output ports of the MCU-system.

Robust Single-Trial EEG-Based Authentication Achieved with a 2-Stage Classifier

The risk of personal data exposure through unauthorized access has never been as imminent as today. To counter this, biometric authentication has been proposed: the use of distinctive physiological and behavioral characteristics as a form of identification and access control. One of the recent developments is electroencephalography (EEG)-based authentication. It builds on the subject-specific nature of brain responses which are difficult to recreate artificially. We propose an authentication system based on EEG signals recorded in response to a simple motor paradigm. Authentication is achieved with a novel two-stage decoder. In the first stage, EEG signal features are extracted using an inception- and a VGG-like deep learning neural network (NN) both of which we compare with principal component analysis (PCA). In the second stage, a support vector machine (SVM) is used for binary classification to authenticate the subject based on the extracted features. All decoders are trained on EEG motor-movement data recorded from 105 subjects. We achieved with the VGG-like NN-SVM decoder a false-acceptance rate (FAR) of 2.55% with an overall accuracy of 88.29%, a FAR of 3.33% with an accuracy of 87.47%, and a FAR of 2.89% with an accuracy of 90.68% for 8, 16, and 64 channels, respectively. With the Inception-like NN-SVM decoder we achieved a false-acceptance rate (FAR) of 4.08% with an overall accuracy of 87.29%, a FAR of 3.53% with an accuracy of 85.31%, and a FAR of 1.27% with an accuracy of 93.40% for 8, 16, and 64 channels, respectively. The PCA-SVM decoder achieved accuracies of 92.09%, 92.36%, and 95.64% with FARs of 2.19%, 2.17%, and 1.26% for 8, 16, and 64 channels, respectively.

An Enhanced Computational Fusion Technique for Security of Authentication of Electronic Voting System

The detection of a false individual who had not been enrolled as a genuine participant in an election could be potentially detected in electronic voting systems as against paper-based methods. In recent time, one-time password and biometrics have been used to curtail false acceptance of imposters. However, imposters had unlawfully stolen the credentials of genuine individuals, gained unauthorized access, and polled illegitimate votes due to poor authentication methodology. The accuracy of a multi-biometric system is a function of the data type used and fusion method adopted. This paper presented a computational fusion approach that involved the use of fingerprint and randomly generated voter identification number to effectively satisfy the authentication security requirement of the electronic voting system. New architectural and mathematical equations on the proposed approach were presented to tackle the problem of false acceptance rate and improve on the true acceptance rate of a biometric system. Algorithm to achieve the proposed approach was presented in this paper as well.

Mouse Dynamics as Continuous User Authentication Tool

User Specific traits are a very strong methodto strengthen the security of any system as it makes thesystem connected to a specific individual instead of beingaccessed through some token, key, etc. Behavior-based user authentication with pointing devices, such as touchpads or mice, has been obtaining attention. Mouse Dynamicsis method which is inexpensive and provide uniquecharacteristic to prevent unlocked workstations attacks tolock out unauthorized users from accessing the system. Aperceptive survey with comparison on mouse dynamicsbiometrics study performed till now is the objective of thispaper. We consider here the best results reported in terms of FalseRejection Rate (FRR) &False Acceptance Rate (FAR).