Multifactor Authentication

Authentication can be accomplished with one factor, two factors, or multiple factors. Which one is the weakest level of authentication and which is the most secure and why? When would a more secure system be required? Be able to explain these multifactor authentication methods: password protection, token presence, voice biometrics, facial recognition, ocular-based methodology, hand geometry, vein recognition, fingerprint scanner, thermal image recognition, and geographical location. What are some challenges of multiple factor authentication when using biometrics? There is a lot of interesting information covered in this article that you do not need to memorize, but that you should be aware of.

3. MFA Operation Challenges

3.4. Robustness to Operating Environment

Even if the security and privacy aspects are fully resolved, the biometric systems, mainly fingerprinting, were falling short of fulfilling the "robustness" requirement since the very beginning of their journey. This was mainly due to the operational trials being conducted in the laboratory environment instead of the field tests. One distinct example is voice recognition, which was highly reliable in a silent room but failed to verify the user in urban landscapes.

A similar problem applies to early facial recognition techniques, which failed to operate without adequate light support, quality camera, etc.. The flip side of the coin was the need for continuous supervision of the examined subject. Even today, there are either bits of advice on where to look/place fingers, or there is visual aid available during the security check. The lack of experience in machine-to-human interaction is commonly analyzed with Failure to Enroll (FTE) as well as Failure to Acquire (FTA) rates. They both depend on the users themselves as well as the additive environmental noise.

Since a significant part of MFA is highly dependent on biometry, it could be classified as inherently probabilistic due to such nature. The base of the biometric authentication lies in the field of pattern matching, which in turn relies on approximation. Approximate matching is a critical consideration in any MFA system, since difference between users could be crucial due to a variety of factors and uncertainty. The image captured during a fingerprint scan would be different every time it is observed because of the presentation angle, pressure, dirt, moisture, or differentiation of sensors even if taken of the same person.

Two important error rates used to quantify the performance of a biometric authentication system are FAR and FRR. FAR is the percentage of impostors inaccurately allowed as genuine users. It is defined as the ratio of the number of false matches to the total number of impostor match attempts. FRR is the number of genuine users rejected from using the system, which is defined as the ratio of the number of false rejections to the total number of genuine match attempts.

Literature further recommends the utilization of the Crossover Error Rate (CER) in addition to the previously discussed metrics. This parameter is defined as the probability of the system being in a state where FAR equals to FRR. The lower this value is, the better the system performs. According to, "Higher FAR is preferred in systems where security is not of prime importance, whereas higher FRR is preferred in high-security applications". The point of equality between FAR and FRR is referred to as Equal Error Rate (EER). Based on the above, it could be once again concluded that a system utilizing solely biometrics may not be considered as a preferred MFA framework.

By analyzing the above listed challenges, it is possible to evaluate and assess the entire MFA system. In what follows, we propose an approach to enable MFA for vehicular integration based on the availability of a large number of sensors in modern vehicles.