Investigation of the Impact of Damaged Smartphone Sensors' Readings on the Quality of Behavioral Biometric Models

Cybersecurity companies from around the world use state-of-the-art technology to provide the best protection against malicious software. Recent times have seen behavioral biometry becoming one of the most popular and widely used components in MFA (Multi-Factor Authentication). The effectiveness and lack of impact on UX (User Experience) is making its popularity rapidly increase among branches in the area of confidential data handling, such as banking, insurance companies, the government, or the military. Although behavioral biometric methods show a high degree of protection against fraudsters, they are susceptible to the quality of input data. The selected behavioral biometrics are strongly dependent on mobile phone IMU sensors. This paper investigates the harmful effects of gaps in data on the behavioral biometry model's accuracy in order to propose suitable countermeasures for this issue.

WoX+: A Meta-Model-Driven Approach to Mine User Habits and Provide Continuous Authentication in the Smart City

The literature is rich in techniques and methods to perform Continuous Authentication (CA) using biometric data, both physiological and behavioral. As a recent trend, less invasive methods such as the ones based on context-aware recognition allows the continuous identification of the user by retrieving device and app usage patterns. However, a still uncovered research topic is to extend the concepts of behavioral and context-aware biometric to take into account all the sensing data provided by the Internet of Things (IoT) and the smart city, in the shape of user habits. In this paper, we propose a meta-model-driven approach to mine user habits, by means of a combination of IoT data incoming from several sources such as smart mobility, smart metering, smart home, wearables and so on. Then, we use those habits to seamlessly authenticate users in real time all along the smart city when the same behavior occurs in different context and with different sensing technologies. Our model, which we called WoX+, allows the automatic extraction of user habits using a novel Artificial Intelligence (AI) technique focused on high-level concepts. The aim is to continuously authenticate the users using their habits as behavioral biometric, independently from the involved sensing hardware. To prove the effectiveness of WoX+ we organized a quantitative and qualitative evaluation in which 10 participants told us a spending habit they have involving the use of IoT. We chose the financial domain because it is ubiquitous, it is inherently multi-device, it is rich in time patterns, and most of all it requires a secure authentication. With the aim of extracting the requirement of such a system, we also asked the cohort how they expect WoX+ will use such habits to securely automatize payments and identify them in the smart city. We discovered that WoX+ satisfies most of the expected requirements, particularly in terms of unobtrusiveness of the solution, in contrast with the limitations observed in the existing studies. Finally, we used the responses given by the cohorts to generate synthetic data and train our novel AI block. Results show that the error in reconstructing the habits is acceptable: Mean Squared Error Percentage (MSEP) 0.04%.

Augmented PIN Authentication through Behavioral Biometrics

Personal Identification Numbers (PINs) are widely used today for user authentication on mobile devices. However, this authentication method can be subject to several attacks such as phishing, smudge, and side-channel. In this paper, we increase the security of PIN-based authentication by considering behavioral biometrics, specifically the smartphone movements typical of each user. To this end, we propose a method based on anomaly detection that is capable of recognizing whether the PIN is inserted by the smartphone owner or by an attacker. This decision is taken according to the smartphone movements, which are recorded during the PIN insertion through the built-in motion sensors. For each digit in the PIN, an anomaly score is computed using Machine Learning (ML) techniques. Subsequently, these scores are combined to obtain the final decision metric. Numerical results show that our authentication method can achieve an Equal Error Rate (EER) as low as 5% in the case of 4-digit PINs, and 4% in the case of 6-digit PINs. Considering a reduced training set, composed of solely 50 samples, the EER only slightly worsens, reaching 6%. The practicality of our approach is further confirmed by the low processing time required, on the order of fractions of milliseconds.

Biometric Identification from Human Aesthetic Preferences

In recent years, human–machine interactions encompass many avenues of life, ranging from personal communications to professional activities. This trend has allowed for person identification based on behavior rather than physical traits to emerge as a growing research domain, which spans areas such as online education, e-commerce, e-communication, and biometric security. The expression of opinions is an example of online behavior that is commonly shared through the liking of online images. Visual aesthetic is a behavioral biometric that involves using a person’s sense of fondness for images. The identification of individuals using their visual aesthetic values as discriminatory features is an emerging domain of research. This paper introduces a novel method for aesthetic feature dimensionality reduction using gene expression programming. The proposed system is capable of using a tree-based genetic approach for feature recombination. Reducing feature dimensionality improves classifier accuracy, reduces computation runtime, and minimizes required storage. The results obtained on a dataset of 200 Flickr users evaluating 40,000 images demonstrate a 95% accuracy of identity recognition based solely on users’ aesthetic preferences.

ADLAuth: Passive Authentication Based on Activity of Daily Living Using Heterogeneous Sensing in Smart Cities

The Internet of Things is a rapidly growing paradigm for smart cities that provides a way of communication, identification, and sensing capabilities among physically distributed devices. With the evolution of the Internet of Things (IoTs), user dependence on smart systems and services, such as smart appliances, smartphone, security, and healthcare applications, has been increased. This demands secure authentication mechanisms to preserve the users’ privacy when interacting with smart devices. This paper proposes a heterogeneous framework “ADLAuth” for passive and implicit authentication of the user using either a smartphone’s built-in sensor or wearable sensors by analyzing the physical activity patterns of the users. Multiclass machine learning algorithms are applied to users’ identity verification. Analyses are performed on three different datasets of heterogeneous sensors for a diverse number of activities. A series of experiments have been performed to test the effectiveness of the proposed framework. The results demonstrate the better performance of the proposed scheme compared to existing work for user authentication.

Authentication of Smartphone Users Based on Activity Recognition and Mobile Sensing

Smartphones are context-aware devices that provide a compelling platform for ubiquitous computing and assist users in accomplishing many of their routine tasks anytime and anywhere, such as sending and receiving emails. The nature of tasks conducted with these devices has evolved with the exponential increase in the sensing and computing capabilities of a smartphone. Due to the ease of use and convenience, many users tend to store their private data, such as personal identifiers and bank account details, on their smartphone. However, this sensitive data can be vulnerable if the device gets stolen or lost. A traditional approach for protecting this type of data on mobile devices is to authenticate users with mechanisms such as PINs, passwords, and fingerprint recognition. However, these techniques are vulnerable to user compliance and a plethora of attacks, such as smudge attacks. The work in this paper addresses these challenges by proposing a novel authentication framework, which is based on recognizing the behavioral traits of smartphone users using the embedded sensors of smartphone, such as Accelerometer, Gyroscope and Magnetometer. The proposed framework also provides a platform for carrying out multi-class smart user authentication, which provides different levels of access to a wide range of smartphone users. This work has been validated with a series of experiments, which demonstrate the effectiveness of the proposed framework.