Special Issue "Machine Learning Approaches for User Identity"
Deadline for manuscript submissions: 30 September 2021.
Interests: cyber Identity; cybersecurity; biometrics; data science; machine learning
Interests: data management; data security; data science; information analysis; information retrieval
Identity theft intimidates the growth of e-commerce and online financial and government services, and a more comprehensive approach is required to protect personal information. User identity plays a critical role in building a secure, trustworthy and privacy-enhanced identity system. The state-of-the-art approaches used to manage identities cannot endure the increasing number of cyber-attacks from expert criminals. Further research is required on the secure control of identity, enhancing interoperability, and providing end users with more direct control over their digital identity. Recent advances in machine learning and artificial intelligence offer successful solutions in designing identity systems.
The Special Issue invites original research papers on the applications of machine learning techniques in biometrics, cyber identity, and identity management. Relevant topics include, but are not limited to:
- Machine learning and deep learning techniques in biometrics and cyber identity;
- Machine learning, neural netwoks and artificial intelligence methods in identity management;
- Privacy, convenience and security of identity systems;
- Human behavior analysis and behavior modeling;
- Continous authentication and authorization;
- Identity ecosystem for the Internet of Things (IoT);
- Adversarial attacks on user authentication;
- Adversarial learning;
- Securing the identity claim using blockchain.
Dr. Kaushik Roy
Dr. Mustafa Atay
Dr. Ajita Rattani
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- physiological and behavioral biometrics
- adversarial biometric recognition
- continuous authentication
- biometric data privacy
- social and mobile biometrics
- human behavior analysis and modeling
- identity management
- face recognition
- iris recognition
- gait recognition
- fingerprint recognition
- spoofing attacks
- presentation attacks
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Abstract: Human activity recognition is attracting interest from researchers and developers in recent years due to its immense applications in wide area of human endeavors. The main issue in human behaviour modeling is represented by the diverse nature of human activities and the nature in which they are performed by the individual makes them challenging to recognize. In this paper we propose a method aimed to recognize human activities and detect users using features gathered from accelerometer sensors widespread in wearable and mobile devices. We exploit machine learning aimed to build models with the ability to discriminate between a set of user activities: sitting, sitting down, standing, standing up and walking. Furthermore, we demonstrate that the proposed method is able to distinguish between different users and to identify the user genre. Real-world experiment shows the effectiveness of the proposed solution.