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

Continuous and transparent multimodal authentication: reviewing the state of the art

Individuals, businesses and governments undertake an ever-growing range of activities online and via various Internet-enabled digital devices. Unfortunately, these activities, services, information and devices are the targets of cybercrimes. Verifying the user legitimacy to use/access a digital device or service has become of the utmost importance. Authentication is the frontline countermeasure of ensuring only the authorized user is granted access; however, it has historically suffered from a range of issues related to the security and usability of the approaches. They are also still mostly functioning at the point of entry and those performing sort of re-authentication executing it in an intrusive manner. Thus, it is apparent that a more innovative, convenient and secure user authentication solution is vital. This paper reviews the authentication methods along with the current use of authentication technologies, aiming at developing a current state-of-the-art and identifying the open problems to be tackled and available solutions to be adopted. It also investigates whether these authentication technologies have the capability to fill the gap between high security and user satisfaction. This is followed by a literature review of the existing research on continuous and transparent multimodal authentication. It concludes that providing users with adequate protection and convenience requires innovative robust authentication mechanisms to be utilized in a universal level. Ultimately, a potential federated biometric authentication solution is presented; however it needs to be developed and extensively evaluated, thus operating in a transparent, continuous and user-friendly manner

Features extraction scheme for behavioral biometric authentication in touchscreen mobile devices

Common authentication mechanisms in mobile devices such as passwords and Personal Identification Number have failed to keep up with the rapid pace of challenges associated with the use of ubiquitous devices over the Internet, since they can easily be lost or stolen. Thus, it is important to develop authentication mechanisms that can be adapted to such an environment. Biometric-based person recognition is a good alternative to overcome the difficulties of password and token approaches, since biometrics cannot be easily stolen or forgotten. An important characteristic of biometric authentication is that there is an explicit connection with the user's identity, since biometrics rely entirely on behavioral and physiological characteristics of human being. There are a variety of biometric authentication options that have emerged so far, all of which can be used on a mobile phone. These options include but are not limited to, face recognition via camera, fingerprint, voice recognition, keystroke and gesture recognition via touch screen. Touch gesture behavioural biometrics are commonly used as an alternative solution to existing traditional biometric mechanism. However, current touch gesture authentication schemes are fraught with authentication accuracy problems. In fact, the extracted features used in some researches on touch gesture schemes are limited to speed, time, position, finger size and finger pressure. However, extracting a few touch features from individual touches is not enough to accurately distinguish various users. In this research, behavioural features are extracted from recorded touch screen data and a discriminative classifier is trained on these extracted features for authentication. While the user performs the gesture, the touch screen sensor is leveraged on and twelve of the user‘s finger touch features are extracted. Eighty four different users participated in this research work, each user drew six gesture with a total of 504 instances. The extracted touch gesture features are normalised by scaling the values so that they fall within a small specified range. Thereafter, five different Feature Selection Algorithm were used to choose the most significant features subset. Six different machine learning classifiers were used to classify each instance in the data set into one of the predefined set of classes. Results from experiments conducted in the proposed touch gesture behavioral biometrics scheme achieved an average False Reject Rate (FRR) of 7.84%, average False Accept Rate (FAR) of 1%, average Equal Error Rate (EER) of 4.02% and authentication accuracy of 91.67%,. The comparative results showed that the proposed scheme outperforms other existing touch gesture authentication schemes in terms of FAR, EER and authentication accuracy by 1.67%, 6.74% and 4.65% respectively. The results of this research affirm that user authentication through gestures is promising, highly viable and can be used for mobile devices