A Survey on Adaptive Authentication Using Machine Learning Techniques

Title

A Survey on Adaptive Authentication Using Machine Learning Techniques

Creator

Pramila R.M.; Misbahuddin M.; Shukla S.

Description

Adaptive authentication is a reliable technique to dynamically select the best mechanisms among multiple modalities to authenticate a user based on the users risk profile generated using behavior and context-based information. Websites or enterprise applications enabled with adaptive authentication will have a more robust security system as analyzing the large volume of the user, device, and browser data in real time generates a risk score that decides the appropriate level of security. Though a significant amount of research is being carried out on adaptive authentication, no single model is suitable for a global attack. This paper provides a structured (extensive) survey of current adaptive authentication techniques available in the literature to identify the challenges which demand future research. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Source

Lecture Notes in Networks and Systems, Vol-462, pp. 317-335.

Date

2022-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Behavior-based fingerprinting; Browser fingerprinting; Context-based fingerprinting; Risk-based authentication; Security and privacy

Coverage

Pramila R.M., Christ University, Bangalore, India; Misbahuddin M., Centre for Development and Advanced Computing (CDAC), Electronics City, Bangalore, India; Shukla S., Christ University, Bangalore, India

Rights

Restricted Access

Relation

ISSN: 23673370; ISBN: 978-981192210-7

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-19-2211-4_28

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135096913&doi=10.1007%2f978-981-19-2211-4_28&partnerID=40&md5=183355bc1c1500ac57bbed27c64c7cdf