User authentication based on foot motion

The paper is intended as a short review of user authentication problem for mobile devices. The emphasis is put on smartphones and tablets, that nowadays are very similar to miniaturized personal computers with much more sensors of various origin. The sensors are described with remarks on their usefulness for user authentication. Deficiencies of traditional user authentication methods based on knowledge are pointed out and the need for new – more secure but also comfortable – user authentication mechanisms is reasoned. Preliminary user authentication systems employing biometric features are discussed and hence the generally unused potential of biometrics for mobile devices is demonstrated.

Behavior-based user authentication has arisen as a viable method for strengthening cybersecurity in an age of pervasive wearable and mobile technologies. This research introduces an innovative approach for ongoing user authentication via behavioral biometrics obtained from wearable sensors. We present a hybrid deep learning network called SE-DeepConvNet, which integrates a squeeze-and-excitation (SE) method to proficiently simulate and authenticate user behavior characteristics. Our methodology utilizes data collected by wearable sensors, such as accelerometers, gyroscopes, and magnetometers, to obtain a thorough behavioral appearance. The suggested network design integrates convolutional neural networks for spatial feature extraction, while the SE blocks improve feature identification by flexibly recalibrating channel-wise feature responses. Experiments performed on two datasets, HMOG and USC-HAD, indicate the efficacy of our technique across different tasks. In the HMOG dataset, SE-DeepConvNet attains a minimal equal error rate (EER) of 0.38% and a maximum accuracy of 99.78% for the Read_Walk activity. Our model presents outstanding authentication (0% EER, 100% accuracy) for various walking activities in the USC-HAD dataset, encompassing intricate situations such as ascending and descending stairs. These findings markedly exceed existing deep learning techniques, demonstrating the promise of our technology for secure and inconspicuous continuous authentication in wearable devices. The suggested approach demonstrates the potential for use in individual device security, access management, and ongoing uniqueness verification in sensitive settings.

On wearable and Virtual Reality (VR) platforms, user authentication is a basic function, but usually a keyboard or touchscreen cannot be provided to type a password. Hand gesture and especially in-air-handwriting can be potentially used for user authentication because a gesture input interface is readily available on these platforms. However, determining whether a login request is from the legitimate user based on a piece of hand movement is challenging in both signal processing and matching, which leads to limited performance in existing systems. In this paper, we propose a multifactor user authentication framework using both the motion signal of a piece of in-air-handwriting and the geometry of hand skeleton captured by a depth camera. To demonstrate this framework, we invented a signal matching algorithm, implemented a prototype, and conducted experiments on a dataset of 100 users collected by us. Our system achieves 0.6% Equal Error Rate (EER) without spoofing attack and 3.4% EER with spoofing only data, which is a significant improvement compared to existing systems using the Dynamic Time Warping (DTW) algorithm. In addition, we presented an in-depth analysis of the utilized features to explain the reason for the performance boost.

SUMMARYWith the use of smart card in user authentication mechanisms, the concept of two‐factor authentication came into existence. This was a forward move towards more secure and reliable user authentication systems. It elevated the security level by requiring a user to possess something in addition to know something. In 2010, Sood et al. and Song independently examined a smart‐card‐based authentication scheme proposed by Xu et al. They showed that in the scheme of Xu et al., an internal user of the system can turn hostile to impersonate other users of the system. Both of them also proposed schemes to improve the scheme of Xu et al. Recently, Chen et al. identified some security problems in the improved schemes proposed by Sood et al. and Song. To fix these problems, Chen et al. presented another scheme, which they claimed to provide mutual authentication and withstand lost smart card attack. Undoubtedly, in their scheme, a user can also verify the legitimacy of server, but we find that the scheme fails to resist impersonation attacks and privileged insider attack. We also show that the scheme does not provide important features such as user anonymity, confidentiality to air messages, and revocation of lost/stolen smart card. Besides, the scheme defies the very purpose of two‐factor security. Furthermore, an attacker can guess a user's password from his or her lost/stolen smart card. To meet these challenges, we propose a user authentication method with user anonymity. We show through analysis and comparison that the proposed scheme exhibits enhanced efficiency in contrast to related schemes, including the scheme of Chen et al. Copyright © 2013 John Wiley & Sons, Ltd.

Openness has been added to IPTV (Internet Protocol Television) services in order for users not only to receive, but also to provide them. Open IPTV is expected to bring about great changes in the IPTV service area. Recently, mobility has also been added. However, because of its openness and IP network characteristics, security is one of the major issues hindering its commercialization. For IPTV, several security mechanisms for user authentication and content protection have been proposed to make the service reliable. However, these traditional security mechanisms cannot be adopted in the mobile IPTV area, because the security and system performance are influenced by numerous factors in the many-to-many environments. In our work, we propose a mechanism for secure user authentication and key distribution based on Kerberos for mobile open IPTV. Our proposal provides an efficient authentication process and the secure transmission of content among users, and further decreases the authentication time compared w...

The Internet of Things (IOT) has ubiquitous capabilities of identi cation,sensing and communication. Body Area Network (BAN) belongs to IOT eld related to the human body. It is widely used in e-health applications, helping people monitor their activities in their daily lives. Walking is the basic part of many daily activities, hence gait analysis can provide important information about the physiological behavior in BAN applications. Many existing researches on gait analysis have a good results, but there are still some problems. For example, most gait feature extraction are carried out by more than 6 conversions for acceleration signal, so that the characteristic dimension reaches 45 or more. Therefore, they need to simplify the features by dimension-reduction or optimization, which makes them very complex. In this paper, we rstly design a exible and convenient data acquisition system. Then we use Wavelet Transform, Fourier Transform and Interquartile Range to extract some simple, low-dimensional motion characteristics of acceleration signal to re ect the characteristics of the movement. Finally, we make recognition and veri cation of gait behavior by pattern recognition algorithms. Experimental results show that the system is easy to use and the method of feature extraction is simple and practical. And its recognition accuracy reaches 97% and minimum EER (Equal Error Rate) reaches 0.9%.

Keystroke dynamics is one solution to enhance the security of password authentication without adding any disruptive handling for users. Industries are looking for more security without impacting too much user experience. Considered as a friction-less solution, keystroke dynamics is a powerful solution to increase trust during user authentication without adding charge to the user. In this paper, we address the problem of user authentication considering the keystroke dynamics modality. We proposed a new approach based on the conversion of behavioral biometrics data (time series) into a 3D image. This transformation process keeps all the characteristics of the behavioral signal. The time series do not receive any filtering operation with this transformation and the method is bijective. This transformation allows us to train images based on convolutional neural networks. We evaluate the performance of the authentication system in terms of Equal Error Rate (EER) on a significant dataset and we show the efficiency of the proposed approach on a multi-instance system.

One of the most commonly used user authentication mechanisms is two factor authentication based on smart card and password. The core feature of the scheme is to enforce that the user must have the smart card and know the password in order to gain access to server. Recently, Liu et al. proposed a smart card based password authentication scheme and argued that it is secure against insider attack, replay attack and man in the middle attack and provides perfect forward secrecy. In this paper, we show security weaknesses in Liu et al.’s scheme focused on off-line password guessing attack and masquerading attack and it does not provide perfect forward secrecy and anonymity. Accordingly, we propose a privacy preserving user authentication scheme based on smart card, denoted as PUAS, to remedy these security weaknesses and to provide anonymity and perfect forward secrecy. PUAS is more secure with a bit of computational overhead to support several positive properties in security and privacy.

Purpose Gesture recognition based on biological signals played a crucial role in the field of human-computer interaction. Despite achieving decent results in improving gesture recognition accuracy using different algorithms, significant challenges remained in enhancing recognition accuracy for multimodal signals and special patients such as upper limb amputees. Methods This paper proposed a Broad Learning System (BLS) method to recognize hand movements. The input signals fused surface electromyographic (sEMG) and acceleration signals, and the fused data were mapped into contour maps to extract features, which were transformed into two-dimensional grayscale images to achieve efficient encoding. The paper utilized the public databases "Ninapro DB2 and DB7" to evaluate the performance of the proposed method. Results Experimental results showed that the average gesture recognition accuracy of BLS reached 97.73%, with an average testing time of 0.093s, outperforming the other two algorithms, K-Nearest Neighbor (0.497s) and Binary Tree (2.412s). Moreover, the fused signals exhibited higher recognition accuracy for amputees. Conclusion In conclusion, the accuracy and real-time of the method could satisfy the requirements for controlling the prosthetic. Hence, it provided a reliable approach for research in fields such as rehabilitative medicine and prosthetic control.

Consortium blockchain has been widely used in many application scenarios, where there is the demand for a universal user authentication and key exchange mechanism for all the application users in the system like Know Your Customer. Since current solutions heavily rely on traditional public-key cryptosystems that are vulnerable to attacks from quantum computers, we design and implement the first post-quantum (PQ) user authentication and key exchange system for consortium blockchain, which is integrated with all the PQ public-key (i.e., signature and encryption/KEM) algorithms in the current round of NIST call for national standard. Furthermore, we also provide chaincodes, related APIs together with client codes for further development. Last but not least, we perform a systematic evaluation on the performance of the system including the consumed time of chaincodes execution and the needed on-chain storage space. Based on the experiment results, we discuss the implications of our findings, which are helpful for the PQ blockchain-based application developers, the undergoing NIST call and the developers of the PQ algorithms.

Keystroke dynamics is a form of behavioral biometrics that can be used for continuous authentication of computer users. Many classifiers have been proposed for the analysis of acquired user patterns and verification of users at computer terminals. The underlying machine learning methods that use Gaussian density estimator for outlier detection typically assume that the digraph patterns in keystroke data are generated from a single Gaussian distribution. In this paper, we relax this assumption by allowing digraphs to fit more than one distribution via the Gaussian Mixture Model (GMM). We have conducted an experiment with a public data set collected in a controlled environment. Out of 30 users with dynamic text, we obtain 0.08% Equal Error Rate (EER) with 2 components by using GMM, while pure Gaussian yields 1.3% EER for the same data set (an improvement of EER by 93.8%). Our results show that GMM can recognize keystroke dynamics more precisely and authenticate users with higher confidence level.

Voice biometrics is widely adopted for identity authentication in mobile devices. However, voice authentication is vulnerable to spoofing attacks, where an adversary may deceive the voice authentication system with pre-recorded or synthesized samples from the legitimate user or by impersonating the speaking style of the targeted user. In this paper, we design and implement VoicePop, a robust software-only anti-spoofing system on smartphones. VoicePop leverages the pop noise, which is produced by the user breathing while speaking close to the microphone. The pop noise is delicate and subject to user diversity, making it hard to record by replay attacks beyond a certain distance and to imitate precisely by impersonators. We design a novel pop noise detection scheme to pinpoint pop noises at the phonemic level, based on which we establish individually unique relationship between phonemes and pop noises to identify legitimate users and defend against spoofing attacks. Our experimental results with 18 participants and three types of smartphones show that VoicePop achieves over 93.5% detection accuracy at around 5.4% equal error rate. VoicePop requires no additional hardware but only the built-in microphones in virtually all smartphones, which can be readily integrated in existing voice authentication systems for mobile devices.

Session authentication schemes establish the identity of the user only at the beginning of the session, so they are vulnerable to attacks that tamper with communications after the establishment of the authenticated session. Moreover, smartphones themselves are used as authentication means, especially in two-factor authentication schemes, which are often required by several services. Whether the smartphone is in the hands of the legitimate user constitutes a great concern, and correspondingly whether the legitimate user is the one who uses the services. In response to these concerns, Behavioral Biometrics (BB) Continuous Authentication (CA) technologies have been proposed on a large corpus of literature. This paper presents a research on the development and validation of a BBCA system (named BioPrivacy), that is based on the user’s keystroke dynamics, using a Multi-Layer Perceptron (MLP). Also, we introduce a new behavioral biometrics collection tool, and we propose a methodology for the selection of an appropriate set of behavioral biometrics. Our system achieved 97.18% Accuracy, 0.02% Equal Error Rate (EER), 97.2% True Acceptance Rate (TAR) and 0.02% False Acceptance Rate (FAR).KeywordsMachine learningBehavioral biometricsContinuous authenticationMobile devicesMulti-layer perceptron (MLP)

One of the most important requirements on a biometric verification system, beside others (e.g. biometric template protection), is a high user authentication performance. During the last years a lot of research is done in different domains to improve user authentication performance. In this work we suggest a user specific feature mask vector MV applied on a biometric hash algorithm for dynamic handwriting to improve user authentication and hash generation performance. MV is generated using an additional set of reference data in order to select/deselect certain features used during the verification process. Therefore, this method is considered as a simple feature selection strategy and is applied for every user within the system. In our first experiments we evaluate 5850 raw data samples captured from 39 users for five different semantics. Semantics are alternative written content to conceal the real identity of a user. First results show a noticeable decrease of the equal error rate by approximately three percentage points for each semantic. Lowest equal error rate (5.77%) is achieved by semantic symbol. In the context of biometric hash generation, the reproduction rates (RR) increases by an average of approx. 26%, whereas the highest RR (88.46%) is obtained by semantic symbol along with a collision rate (CR) of 5.11%. The minimal amount of selected features during the evaluation is 81 and the maximum amount is 131 (all available features).

Behavior biometrics-based user authentication with Wi-Fi gains significant attention due to its ubiquitous and contact-free manners. An individual’s identity can be verified by analyzing activities induced signal variances, excellently balancing the security demands and user experience. However, the inherent complexity of Wi-Fi signals presents significant challenges for behavior biometrics-based user authentication. The susceptibility of Wi-Fi signals results in a poor cross-environment generalization capability, which is overlooked by the existing research. In addition, most existing works of behavior-based user authentication are based on one-off activity. This makes them vulnerable to zero-effort attacks and imitation attacks. To address these issues, we propose a cross-environment continuous gesture-based user authentication framework with Wi-Fi, dubbed Wi-CGAuth. Specifically, the cross-environment generalization capability is enhanced by the cross-layer joint optimization approach. At the lowest signal layer, the signals’ time, spatial, and frequency diversity are extended maximally, by a novel, subcarrier-level, cost-effective signal optimization strategy. At the middle layer, the multi-view fusion method, i.e., multi-transfer component analysis (TCA), is applied to refine the signals from transceiver pairs after signal preprocessing. The continuous gesture segmentation problem is modeled as the classification problem, which is solved by CNN. At the upper layer, a Convolutional Neural Network-Transformer (CNN-Transformer) model is employed to achieve the dual task of effective user authentication and accurate gesture recognition. After extensive experiments in three typical indoor scenarios, Wi-CGAuth can achieve an average authentication accuracy of 92.7%, demonstrating its robustness and effectiveness.