Keystroke Biometrics in Response to Fake News Propagation in a Global Pandemic

This work proposes and analyzes the use of keystroke biometrics for content de-anonymization. Fake news have become a powerful tool to manipulate public opinion, especially during major events. In particular, the massive spread of fake news during the COVID-19 pandemic has forced governments and companies to fight against missinformation. In this context, the ability to link multiple accounts or profiles that spread such malicious content on the Internet while hiding in anonymity would enable proactive identification and blacklisting. Behavioral biometrics can be powerful tools in this fight. In this work, we have analyzed how the latest advances in keystroke biometric recognition can help to link behavioral typing patterns in experiments involving 100,000 users and more than 1 million typed sequences. Our proposed system is based on Recurrent Neural Networks adapted to the context of content de-anonymization. Assuming the challenge to link the typed content of a target user in a pool of candidate profiles, our results show that keystroke recognition can be used to reduce the list of candidate profiles by more than 90%. In addition, when keystroke is combined with auxiliary data (such as location), our system achieves a Rank-1 identification performance equal to 52.6% and 10.9% for a background candidate list composed of 1K and 100K profiles, respectively.Comment: arXiv admin note: text overlap with arXiv:2004.0362

Web-Based Benchmark for Keystroke Dynamics Biometric Systems: A Statistical Analysis

Most keystroke dynamics studies have been evaluated using a specific kind of dataset in which users type an imposed login and password. Moreover, these studies are optimistics since most of them use different acquisition protocols, private datasets, controlled environment, etc. In order to enhance the accuracy of keystroke dynamics' performance, the main contribution of this paper is twofold. First, we provide a new kind of dataset in which users have typed both an imposed and a chosen pairs of logins and passwords. In addition, the keystroke dynamics samples are collected in a web-based uncontrolled environment (OS, keyboards, browser, etc.). Such kind of dataset is important since it provides us more realistic results of keystroke dynamics' performance in comparison to the literature (controlled environment, etc.). Second, we present a statistical analysis of well known assertions such as the relationship between performance and password size, impact of fusion schemes on system overall performance, and others such as the relationship between performance and entropy. We put into obviousness in this paper some new results on keystroke dynamics in realistic conditions.Comment: The Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP 2012), Piraeus : Greece (2012

CALIPER: Continuous Authentication Layered with Integrated PKI Encoding Recognition

Architectures relying on continuous authentication require a secure way to challenge the user's identity without trusting that the Continuous Authentication Subsystem (CAS) has not been compromised, i.e., that the response to the layer which manages service/application access is not fake. In this paper, we introduce the CALIPER protocol, in which a separate Continuous Access Verification Entity (CAVE) directly challenges the user's identity in a continuous authentication regime. Instead of simply returning authentication probabilities or confidence scores, CALIPER's CAS uses live hard and soft biometric samples from the user to extract a cryptographic private key embedded in a challenge posed by the CAVE. The CAS then uses this key to sign a response to the CAVE. CALIPER supports multiple modalities, key lengths, and security levels and can be applied in two scenarios: One where the CAS must authenticate its user to a CAVE running on a remote server (device-server) for access to remote application data, and another where the CAS must authenticate its user to a locally running trusted computing module (TCM) for access to local application data (device-TCM). We further demonstrate that CALIPER can leverage device hardware resources to enable privacy and security even when the device's kernel is compromised, and we show how this authentication protocol can even be expanded to obfuscate direct kernel object manipulation (DKOM) malwares.Comment: Accepted to CVPR 2016 Biometrics Worksho

Touchalytics: On the Applicability of Touchscreen Input as a Behavioral Biometric for Continuous Authentication

We investigate whether a classifier can continuously authenticate users based on the way they interact with the touchscreen of a smart phone. We propose a set of 30 behavioral touch features that can be extracted from raw touchscreen logs and demonstrate that different users populate distinct subspaces of this feature space. In a systematic experiment designed to test how this behavioral pattern exhibits consistency over time, we collected touch data from users interacting with a smart phone using basic navigation maneuvers, i.e., up-down and left-right scrolling. We propose a classification framework that learns the touch behavior of a user during an enrollment phase and is able to accept or reject the current user by monitoring interaction with the touch screen. The classifier achieves a median equal error rate of 0% for intra-session authentication, 2%-3% for inter-session authentication and below 4% when the authentication test was carried out one week after the enrollment phase. While our experimental findings disqualify this method as a standalone authentication mechanism for long-term authentication, it could be implemented as a means to extend screen-lock time or as a part of a multi-modal biometric authentication system.Comment: to appear at IEEE Transactions on Information Forensics & Security; Download data from http://www.mariofrank.net/touchalytics

On the Design and Analysis of a Biometric Authentication System using Keystroke Dynamics

This paper proposes a portable hardware token for user authentication, it is based on the use of keystroke dynamics to verify users in a bio-metric manner. The proposed approach allows for a multifactor authentication scheme in which users are not allowed access unless they provide the correct password and their unique bio-metric signature. The proposed system is implemented in hardware and its security is evaluated

Enhancing Usability and Security through Alternative Authentication Methods

With the expanding popularity of various Internet services, online users have be- come more vulnerable to malicious attacks as more of their private information is accessible on the Internet. The primary defense protecting private information is user authentication, which currently relies on less than ideal methods such as text passwords and PIN numbers. Alternative methods such as graphical passwords and behavioral biometrics have been proposed, but with too many limitations to replace current methods. However, with enhancements to overcome these limitations and harden existing methods, alternative authentications may become viable for future use. This dissertation aims to enhance the viability of alternative authentication systems. In particular, our research focuses on graphical passwords, biometrics that depend, directly or indirectly, on anthropometric data, and user authentication en- hancements using touch screen features on mobile devices. In the study of graphical passwords, we develop a new cued-recall graphical pass- word system called GridMap by exploring (1) the use of grids with variable input entered through the keyboard, and (2) the use of maps as background images. as a result, GridMap is able to achieve high key space and resistance to shoulder surfing attacks. to validate the efficacy of GridMap in practice, we conduct a user study with 50 participants. Our experimental results show that GridMap works well in domains in which a user logs in on a regular basis, and provides a memorability benefit if the chosen map has a personal significance to the user. In the study of anthropometric based biometrics through the use of mouse dy- namics, we present a method for choosing metrics based on empirical evidence of natural difference in the genders. In particular, we develop a novel gender classifi- cation model and evaluate the model’s accuracy based on the data collected from a group of 94 users. Temporal, spatial, and accuracy metrics are recorded from kine- matic and spatial analyses of 256 mouse movements performed by each user. The effectiveness of our model is validated through the use of binary logistic regressions. Finally, we propose enhanced authentication schemes through redesigned input, along with the use of anthropometric biometrics on mobile devices. We design a novel scheme called Triple Touch PIN (TTP) that improves traditional PIN number based authentication with highly enlarged keyspace. We evaluate TTP on a group of 25 participants. Our evaluation results show that TTP is robust against dictio- nary attacks and achieves usability at acceptable levels for users. We also assess anthropometric based biometrics by attempting to differentiate user fingers through the readings of the sensors in the touch screen. We validate the viability of this biometric approach on 33 users, and observe that it is feasible for distinguishing the fingers with the largest anthropometric differences, the thumb and pinkie fingers

Credential hardening by using touchstroke dynamics

Today, reliance on digital devices for daily routines has been shifted towards portable mobile devices. Therefore, the need for security enhancements within this platform is imminent. Numerous research works have been performed on strengthening password authentication by using keystroke dynamics biometrics, which involve computer keyboards and cellular phones as input devices. Nevertheless, experiments performed specifically on touch screen devices are relatively lacking. This paper describes a novel technique to strengthen security authentication systems on touch screen devices via a new sub variant behavioural biometrics called touchstroke dynamics. We capitalize on the high resolution timing latency and the pressure information on touch screen panel as feature data. Following this a light weight algorithm is introduced to calculate the similarity between feature vectors. In addition, a fusion approach is proposed to enhance the overall performance of the system to an equal error rate of 7.71% (short input) and 6.27% (long input)