| Title | Multi-Factor Authentication to Systems Login |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | ALSaleem, Bandar Omar, Alshoshan, Abdullah I. |
| Conference Name | 2021 National Computing Colleges Conference (NCCC) |
| Keywords | a third-party authenticator (TPA), Education, Graphical Password, Hardware, Human Behavior, human factors, ID, key logger, Keyboards, Knowledge engineering, Metrics, Multi-factor authentication, multi-factor authentication (MFA), multifactor authentication, pubcrawl, resilience, Resiliency, Retina, screen capture, shoulder surfing, Tools |
| Abstract | Multi-Factor Authentication is an electronic authentication method in which a computer user is granted access to an application or a website only after successfully presenting two or more factors, or pieces of evidence. It is the first step to protect systems against intruders since the traditional log-in methods (username and password) are not completely protected from hackers, since they can guess them easily using tools. Current Systems use additional methods to increase security, such as using two-factor authentication based on a one-time password via mobile or email, or authentication based on biometrics (fingerprint, eye iris or retina, and face recognition) or via token devices. However, these methods require additional hardware equipment with high cost at the level of small and medium companies. This paper proposes a multi-factor authentication system that combines ease of use and low-cost factors. The system does not need any special settings or infrastructure. It relies on graphical passwords, so the user, in registration phase, chooses three images and memorizes them. In the login phase, the user needs only to choose the correct images that he considered during the registration process in a specific order. The proposed system overcomes many different security threats, such as key-loggers, screen capture attack or shoulder surfing. The proposed method was applied to 170 participants, 75% of them are males and 25% are females, classified according to their age, education level, web experience. One-third of them did not have sufficient knowledge about various security threats. |
| DOI | 10.1109/NCCC49330.2021.9428806 |
| Citation Key | alsaleem_multi-factor_2021 |
Multi-Factor Authentication to Systems Login