Attacks and defenses in user authentication systems: A survey
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Journal of Network and Computer Applications, Volume 188
AbstractUser authentication systems (in short authentication systems) have wide utilization in our daily life. Unfortunately, existing authentication systems are prone to various attacks while both system security and usability are expected to be satisfied. But the current research still lacks a thorough survey on various types of attacks and corresponding countermeasures regarding user authentication, including traditional password-based and emerging biometric-based systems. In this paper, we make a comprehensive review on attacks and defenses of the authentication systems. We firstly introduce a number of common attacks by classifying them into different categories based on attacker knowledge, attack target, attack form and attack strength. Then, we propose a set of evaluation criteria for evaluating different kinds of attack defense mechanisms. Furthermore, we review and evaluate the existing methods of detecting and resisting attacks in the authentication systems by employing the proposed evaluation criteria as acommon measure. Specifically, we focus on comparing and analyzing the performance of different defense mechanisms in different types of authentication systems. Through serious review and analysis, we put forward a number of open issues and propose some promising future research directions, hoping to inspire further research in this field.
Funding Information: This work was supported in part by the National Natural Science Foundation of China under Grant 62072351 ; in part by the Academy of Finland under Grant 308087 and Grant 335262 ; in part by the Shaanxi Innovation Team Project under Grant 2018TD-007 ; and in part by the 111 Project under Grant B16037 . Publisher Copyright: © 2021 Elsevier Ltd
Attack detection, Authentication system, Biometric authentication, CAPTCHA, Deep neural networks, Defense mechanisms, Liveness detection, Machine learning, Spoofing attack
Wang , X , Yan , Z , Zhang , R & Zhang , P 2021 , ' Attacks and defenses in user authentication systems: A survey ' , Journal of Network and Computer Applications , vol. 188 , 103080 . https://doi.org/10.1016/j.jnca.2021.103080