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A Multi-Feature User Authentication Model Based on Mobile App Interactions
- Source :
- IEEE Access, Vol 8, Pp 96322-96339 (2020)
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- Knowledge-based authentication approaches such as the use of passwords and personal identification numbers (PINs) are the most common ways of authenticating users. The main problem with such approach is relying on simple authentication login credentials at the login stage, and assuming the user is still the same between access sessions makes applications and networks vulnerable to access by unauthorized users. Application-level access patterns on smartphone and tablet devices can be utilized to provide an approach for continuously authenticating and identifying users. This paper presents a user authentication and identification method based on mobile application access patterns, and throughout the paper we use a smart home environment as a motivating scenario. To enhance the classification process, many features have been extracted and utilized which considerably improved differentiating between users and eliminating similarities in the access usage patterns. The proposed model has been evaluated using two datasets, and the results show an ability to authenticate users with high accuracy in terms of low false positive, false negative, and equal error rates. Overall, the statistical analysis of the extracted multi-features and the results show that the feasibility of decision-making based on app interactions can lead to high accuracy.
- Subjects :
- Password
Authentication
User authentication
General Computer Science
business.industry
Computer science
continuous user authentication and identification
General Engineering
Process (computing)
020206 networking & telecommunications
02 engineering and technology
Login
multi-class classification
Identification (information)
Home automation
Human–computer interaction
Mobile app interactions
0202 electrical engineering, electronic engineering, information engineering
smart home networks
020201 artificial intelligence & image processing
General Materials Science
lcsh:Electrical engineering. Electronics. Nuclear engineering
business
lcsh:TK1-9971
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....25440fd74eda7e09f7e534a250185f5a