Multi-Factor Authentication: A Survey †
"> Figure 1
<p>Conceptual authentication examples.</p> "> Figure 2
<p>Evolution of authentication methods from SFA to MFA.</p> "> Figure 3
<p>Main operational challenges of MFA.</p> "> Figure 4
<p>Current and emerging MFA sensors for vehicles.</p> "> Figure 5
<p>Lagrange secret sharing scheme.</p> "> Figure 6
<p>Reversed method based on the Lagrange polynomial.</p> "> Figure 7
<p>Trusted authority assistance in authentication when user is missing two factors.</p> "> Figure 8
<p>MFA system mode. <math display="inline"> <semantics> <msub> <mi>P</mi> <mrow> <mi>T</mi> <mi>H</mi> </mrow> </msub> </semantics> </math> is the selected threshold.</p> "> Figure 9
<p>Biometric MFA for the airport scenario.</p> ">
Abstract
:1. Introduction
- Knowledge factor—something the user knows, such as a password or, simply, a “secret”;
- Ownership factor—something the user has, such as cards, smartphones, or other tokens;
- Biometric factor—something the user is, i.e., biometric data or behavior pattern.
- Customers first register and authenticate with the service provider to activate and manage services they are willing to access;
- Once accessing the service, the user is required to pass a simple SFA with the fingerprint/token signed in advance by the service provider;
- Once initially accepted by the system, the customer authenticates by logging in with the same username and password as setup previously in the customer portal (or social login). For additional security, the managing platform can enable secondary authentication factors. Once the user has successfully passed all the tests, the framework automatically authenticates to the service platform;
- The secondary authentication occurs automatically based on the biometric MFA, so the user would be requested to enter an additional code or provide a token password only in case the MFA fails.
- This work provides a detailed analysis of factors that are presently utilized for MFA with their corresponding operational requirements. Potential sensors to be utilized are surveyed based on the academic and industrial sources (Section 2);
- The survey is followed by the challenges related to MFA adoption from both the user experience and the technological perspectives (Section 3);
- Further, the framework based on the reversed Lagrange polynomial is proposed to allow for utilizing MFA in cases where some of the factors are missing (Section 4). A discussion on the potential evaluation methodology is also provided;
- Finally, the vision of the future of MFA is discussed (Section 5).
2. State-of-the-Art and Potential MFA Sources
2.1. Widely Deployed MFA Sensors/Sources
2.1.1. Password Protection
2.1.2. Token Presence
2.1.3. Voice Biometrics
2.1.4. Facial Recognition
2.1.5. Ocular-Based Methodology
2.1.6. Hand Geometry
2.1.7. Vein Recognition
2.1.8. Fingerprint Scanner
2.1.9. Thermal Image Recognition
2.1.10. Geographical Location
2.2. Future of MFA Integration
2.2.1. Behavior Detection
2.2.2. Beam-Forming Techniques
2.2.3. Occupant Classification Systems (OCS)
2.2.4. Electrocardiographic (ECG) Recognition
2.2.5. Electroencephalographic (EEG) Recognition
2.2.6. DNA Recognition
- Universality stands for the presence of factor in each person;
- Uniqueness indicates how well the factor differentiates one person from another;
- Collectability measures how easy it is to acquire data for processing;
- Performance indicates the achievable accuracy, speed, and robustness;
- Acceptability stands for the degree of acceptance of the technology by people in their daily life;
- Spoofing indicates the level of difficulty to capture and spoof the sample.
3. MFA Operation Challenges
3.1. Usability
- Task efficiency—time to register and time to authenticate with the system;
- Task effectiveness—the number login attempts to authenticate with the system;
- User preference—whether the user prefers a particular authentication scheme over another.
3.2. Integration
3.3. Security and Privacy
3.4. Robustness to Operating Environment
4. Enabling Flexible MFA Operation
4.1. Conventional Approach
4.2. Proposed Reversed Methodology
4.3. Proposed MFA Solution for V2X Applications
4.3.1. Factor Mismatch
4.3.2. Cloud Assistance
4.4. Potential Evaluation Techniques
4.4.1. Strict Decision Methodology
4.4.2. Probabilistic Decision Methodology
4.4.3. Evaluation
5. Discussion and Future Prospects
Acknowledgments
Author Contributions
Conflicts of Interest
Abbreviations
MFA | Multi-Factor Authentication |
SFA | Single-Factor Authentication |
2FA | Two-Factor Authentication |
SSS | Shamir’s Secret Sharing |
PIN | Personal Identification Number |
ID | Identification Number |
ATM | Automated Teller Machine |
FAR | False Accept Rate |
FRR | False Reject Rate |
PPG | Photoplethysmography |
RFID | Radio-Frequency Identification |
NFC | Near-Field Communication |
OCS | Occupant Classification Systems |
ECG | Electrocardiography |
EEG | Electroencephalography |
GPS | Global Positioning System |
FTE | Failure to Enroll |
FTA | Failure to Acquire |
CER | Crossover Error Rate |
EER | Equal Error Rate |
V2X | Vehicle-to-Everything |
IAM | Identity and Access Management |
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Factor | Universality | Uniqueness | Collectability | Performance | Acceptability | Spoofing |
---|---|---|---|---|---|---|
Password | n/a | L | H | H | H | H |
Token | n/a | M | H | H | H | H |
Voice | M | L | M | L | H | H |
Facial | H | L | M | L | H | M |
Ocular-based | H | H | M | M | L | H |
Fingerprint | M | H | M | H | M | H |
Hand geometry | M | M | M | M | M | M |
Location | n/a | L | M | H | M | H |
Vein | M | M | M | M | M | M |
Thermal image | H | H | L | M | H | H |
Behavior | H | H | L | L | L | L |
Beam-forming | n/a | M | L | L | L | H |
OCS | n/a | L | L | L | L | M |
ECG | L | H | L | M | M | L |
EEG | L | H | L | M | L | L |
DNA | H | H | L | H | L | L |
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Ometov, A.; Bezzateev, S.; Mäkitalo, N.; Andreev, S.; Mikkonen, T.; Koucheryavy, Y. Multi-Factor Authentication: A Survey. Cryptography 2018, 2, 1. https://doi.org/10.3390/cryptography2010001
Ometov A, Bezzateev S, Mäkitalo N, Andreev S, Mikkonen T, Koucheryavy Y. Multi-Factor Authentication: A Survey. Cryptography. 2018; 2(1):1. https://doi.org/10.3390/cryptography2010001
Chicago/Turabian StyleOmetov, Aleksandr, Sergey Bezzateev, Niko Mäkitalo, Sergey Andreev, Tommi Mikkonen, and Yevgeni Koucheryavy. 2018. "Multi-Factor Authentication: A Survey" Cryptography 2, no. 1: 1. https://doi.org/10.3390/cryptography2010001
APA StyleOmetov, A., Bezzateev, S., Mäkitalo, N., Andreev, S., Mikkonen, T., & Koucheryavy, Y. (2018). Multi-Factor Authentication: A Survey. Cryptography, 2(1), 1. https://doi.org/10.3390/cryptography2010001