1,099 research outputs found
User Identification and Authentication using Multi-Modal Behavioral Biometrics
Biometric computer authentication has an advantage over password and access card authentication in that it is based on something you are, which is not easily copied or stolen. One way of performing biometric computer authentication is to use behavioral tendencies associated with how a user interacts with the computer. However, behavioral biometric authentication accuracy rates are worse than more traditional authentication methods. This article presents a behavioral biometric system that fuses user data from keyboard, mouse, and Graphical User Interface (GUI) interactions. Combining the modalities results in a more accurate authentication decision based on a broader view of the user\u27s computer activity while requiring less user interaction to train the system than previous work. Testing over 31 users shows that fusion techniques significantly improve behavioral biometric authentication accuracy over single modalities on their own. Between the two fusion techniques presented, feature fusion and an ensemble based classification method, the ensemble method performs the best with a False Acceptance Rate (FAR) of 2.10% and a False Rejection Rate (FRR) 2.24%
Active User Authentication for Smartphones: A Challenge Data Set and Benchmark Results
In this paper, automated user verification techniques for smartphones are
investigated. A unique non-commercial dataset, the University of Maryland
Active Authentication Dataset 02 (UMDAA-02) for multi-modal user authentication
research is introduced. This paper focuses on three sensors - front camera,
touch sensor and location service while providing a general description for
other modalities. Benchmark results for face detection, face verification,
touch-based user identification and location-based next-place prediction are
presented, which indicate that more robust methods fine-tuned to the mobile
platform are needed to achieve satisfactory verification accuracy. The dataset
will be made available to the research community for promoting additional
research.Comment: 8 pages, 12 figures, 6 tables. Best poster award at BTAS 201
Multi-biometric templates using fingerprint and voice
As biometrics gains popularity, there is an increasing concern about privacy and misuse of biometric data held in central repositories. Furthermore, biometric verification systems face challenges arising from noise and intra-class variations. To tackle both problems, a multimodal biometric verification system combining fingerprint and voice modalities is proposed. The system combines the two modalities at the template level, using multibiometric templates. The fusion of fingerprint and voice data successfully diminishes privacy concerns by hiding the minutiae points from the fingerprint, among the artificial points generated by the features obtained from the spoken utterance of the speaker. Equal error rates are observed to be under 2% for the system where 600 utterances from 30 people have been processed and fused with a database of 400 fingerprints from 200 individuals. Accuracy is increased compared to the previous results for voice verification over the same speaker database
Frictionless Authentication Systems: Emerging Trends, Research Challenges and Opportunities
Authentication and authorization are critical security layers to protect a
wide range of online systems, services and content. However, the increased
prevalence of wearable and mobile devices, the expectations of a frictionless
experience and the diverse user environments will challenge the way users are
authenticated. Consumers demand secure and privacy-aware access from any
device, whenever and wherever they are, without any obstacles. This paper
reviews emerging trends and challenges with frictionless authentication systems
and identifies opportunities for further research related to the enrollment of
users, the usability of authentication schemes, as well as security and privacy
trade-offs of mobile and wearable continuous authentication systems.Comment: published at the 11th International Conference on Emerging Security
Information, Systems and Technologies (SECURWARE 2017
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