Strengthen user authentication on mobile devices by using user’s touch dynamics pattern

Mobile devices, particularly the touch screen mobile devices, are increasingly used to store and access private and sensitive data or services, and this has led to an increased demand for more secure and usable security services, one of which is user authentication. Currently, mobile device authentication services mainly use a knowledge-based method, e.g. a PIN-based authentication method, and, in some cases, a fingerprint-based authentication method is also supported. The knowledge-based method is vulnerable to impersonation attacks, while the fingerprint-based method can be unreliable sometimes. To overcome these limitations and to make the authentication service more secure and reliable for touch screen mobile device users, we have investigated the use of touch dynamics biometrics as a mobile device authentication solution by designing, implementing and evaluating a touch dynamics authentication method. This paper describes the design, implementation, and evaluation of this method, the acquisition of raw touch dynamics data, the use of the raw data to obtain touch dynamics features, and the training of the features to build an authentication model for user identity verification. The evaluation results show that by integrating the touch dynamics authentication method into the PIN-based authentication method, the protection levels against impersonation attacks is greatly enhanced. For example, if a PIN is compromised, the success rate of an impersonation attempt is drastically reduced from 100% (if only a 4-digit PIN is used) to 9.9% (if both the PIN and the touch dynamics are used). © 2019, The Author(s)

Sensing Your Touch: Strengthen User Authentication via Touch Dynamic Biometrics

© 2019 IEEE. Mobile devices are increasingly used to store private and sensitive data, and this has led to an increased demand for more secure and usable authentication services. Currently, mobile device authentication services mainly use a knowledge-based method, e.g. a PIN-based authentication method, and, in some cases, a fingerprint-based authentication method is also supported. The knowledge-based method is vulnerable to impersonation attacks, while the fingerprint-based method can be unreliable sometimes. To make the authentication service more secure and reliable for mobile device users, this paper describes our efforts in investigating the benefits of integrating a touch dynamics authentication method into a PIN-based authentication method. It describes the design, implementation and evaluation of this method. Experimental results show that this approach can significantly reduce the success rate of impersonation attempts; in the case of a 4-digit PIN, the success rate is reduced from 100% (if only the PIN is used) to 9.9% (if both the PIN and the touch dynamics are used)

Comparison of Artificial Neural Network and Logistic Regression Models for Predicting In-Hospital Mortality after Primary Liver Cancer Surgery

BACKGROUND: Since most published articles comparing the performance of artificial neural network (ANN) models and logistic regression (LR) models for predicting hepatocellular carcinoma (HCC) outcomes used only a single dataset, the essential issue of internal validity (reproducibility) of the models has not been addressed. The study purposes to validate the use of ANN model for predicting in-hospital mortality in HCC surgery patients in Taiwan and to compare the predictive accuracy of ANN with that of LR model. METHODOLOGY/PRINCIPAL FINDINGS: Patients who underwent a HCC surgery during the period from 1998 to 2009 were included in the study. This study retrospectively compared 1,000 pairs of LR and ANN models based on initial clinical data for 22,926 HCC surgery patients. For each pair of ANN and LR models, the area under the receiver operating characteristic (AUROC) curves, Hosmer-Lemeshow (H-L) statistics and accuracy rate were calculated and compared using paired T-tests. A global sensitivity analysis was also performed to assess the relative significance of input parameters in the system model and the relative importance of variables. Compared to the LR models, the ANN models had a better accuracy rate in 97.28% of cases, a better H-L statistic in 41.18% of cases, and a better AUROC curve in 84.67% of cases. Surgeon volume was the most influential (sensitive) parameter affecting in-hospital mortality followed by age and lengths of stay. CONCLUSIONS/SIGNIFICANCE: In comparison with the conventional LR model, the ANN model in the study was more accurate in predicting in-hospital mortality and had higher overall performance indices. Further studies of this model may consider the effect of a more detailed database that includes complications and clinical examination findings as well as more detailed outcome data

Disease-Free Survival after Hepatic Resection in Hepatocellular Carcinoma Patients: A Prediction Approach Using Artificial Neural Network

Background: A database for hepatocellular carcinoma (HCC) patients who had received hepatic resection was used to develop prediction models for 1-, 3- and 5-year disease-free survival based on a set of clinical parameters for this patient group. Methods: The three prediction models included an artificial neural network (ANN) model, a logistic regression (LR) model, and a decision tree (DT) model. Data for 427, 354 and 297 HCC patients with histories of 1-, 3- and 5-year disease-free survival after hepatic resection, respectively, were extracted from the HCC patient database. From each of the three groups, 80 % of the cases (342, 283 and 238 cases of 1-, 3- and 5-year disease-free survival, respectively) were selected to provide training data for the prediction models. The remaining 20 % of cases in each group (85, 71 and 59 cases in the three respective groups) were assigned to validation groups for performance comparisons of the three models. Area under receiver operating characteristics curve (AUROC) was used as the performance index for evaluating the three models. Conclusions: The ANN model outperformed the LR and DT models in terms of prediction accuracy. This study demonstrated the feasibility of using ANNs in medical decision support systems for predicting disease-free survival based on clinical databases in HCC patients who have received hepatic resection

RNAi in the regulation of mammalian viral infections

Although RNA interference (RNAi) is known to play an important part in defense against viruses of invertebrates, its contribution to mammalian anti-viral defense has been a matter of dispute. This is surprising because all components of the RNAi machinery necessary for robust RNAi-mediated restriction of viruses are conserved in mammals, and the introduction of synthetic small interfering RNAs (siRNAs) into cells efficiently silences the replication of viruses that contain siRNA complementary sequences in those cells. Here, I discuss the reasons for the dispute, and review the evidence that RNAi is a part of the physiological defense of mammalian cells against viral infections

Microfluidic device for robust generation of two-component liquid-in-air slugs with individually controlled composition

Using liquid slugs as microreactors and microvessels enable precise control over the conditions of their contents on short-time scales for a wide variety of applications. Particularly for screening applications, there is a need for control of slug parameters such as size and composition. We describe a new microfluidic approach for creating slugs in air, each comprising a size and composition that can be selected individually for each slug. Two-component slugs are formed by first metering the desired volume of each reagent, merging the two volumes into an end-to-end slug, and propelling the slug to induce mixing. Volume control is achieved by a novel mechanism: two closed chambers on the chip are initially filled with air, and a valve in each is briefly opened to admit one of the reagents. The pressure of each reagent can be individually selected and determines the amount of air compression, and thus the amount of liquid that is admitted into each chamber. We describe the theory of operation, characterize the slug generation chip, and demonstrate the creation of slugs of different compositions. The use of microvalves in this approach enables robust operation with different liquids, and also enables one to work with extremely small samples, even down to a few slug volumes. The latter is important for applications involving precious reagents such as optimizing the reaction conditions for radiolabeling biological molecules as tracers for positron emission tomography

Probabilistic machine learning and artificial intelligence.

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.The author acknowledges an EPSRC grant EP/I036575/1, the DARPA PPAML programme, a Google Focused Research Award for the Automatic Statistician and support from Microsoft Research.This is the author accepted manuscript. The final version is available from NPG at http://www.nature.com/nature/journal/v521/n7553/full/nature14541.html#abstract