Behavioural Authentication Based on Smartphone Protected Personal Communication Data

International audienceSmartphones have become ubiquitous in everyday life, storing and generating a huge amount of sensitive personal data which make them vulnerable to increasing security and privacy threats. While protecting smartphones has become a necessity, existing traditional authentication methods, which are mainly PINs and passwords, are facing remarkable drawbacks and behavioural biometrics-based authentication was adopted as the best alternative to ensure better protection. This paper presents a comparative study of many behavioural authentica-tion solutions using smartphone personal communication data. Different approaches are compared such as using Distance Minimization, K-means and Support Vector Machine (SVM) as classification method. The data privacy protection by using the BioHashing algorithm is also considered in the paper. The authentication approaches were tested on a dataset of 93 users with more than 16.000 samples and show promising results with an EER of 10% without any data protection with the One Class SVM method and an EER remarkably lower than 1% for the 3 adopted methods with data privacy protection

Adaptive algorithms in accelerometer biometrics

Nowadays, many services are available from mobile devices, like smartphones. A growing number of people are using these devices to access bank accounts, social networks and to store personal information. However, common authentication mechanisms already present in these devices may not provide enough security. Recently, a new authentication method, named accelerometer biometrics, has been proposed. This method allows the identification of users using accelerometer data. Accelerometers, usually present in modern smartphones, are devices that measure acceleration forces. In accelerometer biometrics, a model is induced for the user of the smartphone. However, as a behavioral biometric technology, user models may became outdated over time. This paper investigates the use of adaptation mechanisms to update biometric user models induced by accelerometer data along the time. The paper also proposes and evaluates a new adaptation mechanism with promising experimental results.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP

A Light Weight Smartphone Based Human Activity Recognition System with High Accuracy

With the pervasive use of smartphones, which contain numerous sensors, data for modeling human activity is readily available. Human activity recognition is an important area of research because it can be used in context-aware applications. It has significant influence in many other research areas and applications including healthcare, assisted living, personal fitness, and entertainment. There has been a widespread use of machine learning techniques in wearable and smartphone based human activity recognition. Despite being an active area of research for more than a decade, most of the existing approaches require extensive computation to extract feature, train model, and recognize activities. This study presents a computationally efficient smartphone based human activity recognizer, based on dynamical systems and chaos theory. A reconstructed phase space is formed from the accelerometer sensor data using time-delay embedding. A single accelerometer axis is used to reduce memory and computational complexity. A Gaussian mixture model is learned on the reconstructed phase space. A maximum likelihood classifier uses the Gaussian mixture model to classify ten different human activities and a baseline. One public and one collected dataset were used to validate the proposed approach. Data was collected from ten subjects. The public dataset contains data from 30 subjects. Out-of-sample experimental results show that the proposed approach is able to recognize human activities from smartphones’ one-axis raw accelerometer sensor data. The proposed approach achieved 100% accuracy for individual models across all activities and datasets. The proposed research requires 3 to 7 times less amount of data than the existing approaches to classify activities. It also requires 3 to 4 times less amount of time to build reconstructed phase space compare to time and frequency domain features. A comparative evaluation is also presented to compare proposed approach with the state-of-the-art works

Physical activity recognition by utilising smartphone sensor signals

Human physical motion activity identification has many potential applications in various fields, such as medical diagnosis, military sensing, sports analysis, and human-computer security interaction. With the recent advances in smartphones and wearable technologies, it has become common for such devices to have embedded motion sensors that are able to sense even small body movements. This study collected human activity data from 60 participants across two different days for a total of six activities recorded by gyroscope and accelerometer sensors in a modern smartphone. The paper investigates to what extent different activities can be identified by utilising machine learning algorithms using approaches such as majority algorithmic voting. More analyses are also provided that reveal which time and frequency domain-based features were best able to identify individuals’ motion activity types. Overall, the proposed approach achieved a classification accuracy of 98% in identifying four different activities: walking, walking upstairs, walking downstairs, and sitting (on a chair) while the subject is calm and doing a typical desk-based activity

Physical activity recognition by utilising smartphone sensor signals

Human physical motion activity identification has many potential applications in various fields, such as medical diagnosis, military sensing, sports analysis, and human-computer security interaction. With the recent advances in smartphones and wearable technologies, it has become common for such devices to have embedded motion sensors that are able to sense even small body movements. This study collected human activity data from 60 participants across two different days for a total of six activities recorded by gyroscope and accelerometer sensors in a modern smartphone. The paper investigates to what extent different activities can be identified by utilising machine learning algorithms using approaches such as majority algorithmic voting. More analyses are also provided that reveal which time and frequency domain-based features were best able to identify individuals' motion activity types. Overall, the proposed approach achieved a classification accuracy of 98% in identifying four different activities: walking, walking upstairs, walking downstairs, and sitting (on a chair) while the subject is calm and doing a typical desk-based activity

A concept of continuous user authentication based on behavioral biometrics

This paper focuses on continuous user authentication based on its interaction with the device. Behavioral authentication provides the ability to partially abandon passwords. Furthermore, the use of human behavior, for example, how he holds the device in hand, interacts with a screen, as a means of authentication is sufficiently protected from compromise, since an attacker cannot make an exact copy your gait or motion. Use of auxiliary factors such as the proximity of the trusted peripheral device, Wi-Fi network, location, helps simplify authentication

Associations between depression symptom severity and daily-life gait characteristics derived from long-term acceleration signals in real-world settings

Background Gait is an essential manifestation of depression. However, the gait characteristics of daily walking and their relationships with depression are yet to be fully explored. Objective: This study aimed to explore associations between depression symptom severity and daily-life gait characteristics derived from acceleration signals in real-world settings. Methods We used two ambulatory datasets (N=71 and N=215) whose acceleration signals were collected by wearable devices and mobile phones, respectively. We extracted 12 daily-life gait features to describe the distribution and variance of gait cadence and force over a long-term period. Spearman coefficients and linear mixed-effect models were used to explore the associations between daily-life gait features and depression symptom severity measured by GDS-15 and PHQ-8 self-reported questionnaires. The likelihood ratio (LR) test was used to test whether daily-life gait features could provide additional information relative to the laboratory gait features. Results Higher depression symptom severity was found to be significantly associated with lower gait cadence of high-performance walking (segments with faster walking speed) over a long-term period in both datasets. The linear regression model with long-term daily-life gait features ( =0.30) fitted depression scores significantly better (LR test: P value = .001) than the model with only laboratory gait features ( =0.06). Conclusion This study indicated that the significant links between daily-life walking characteristics and depression symptom severity could be captured by both wearable devices and mobile phones. The daily-life gait patterns could provide additional information for predicting depression symptom severity relative to laboratory walking. These findings may contribute to developing clinical tools to remotely monitor mental health in real-world settings