## 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.

### 4.3. Proposed MFA Solution for V2X Applications

#### 4.4.1. Strict Decision Methodology

Each sensor decides whether the user is legitimate or not by returning either accept or reject. The MFA system then combines the collected results and provides a group decision based on the resulting vector. Hence, it is possible to utlize the threshold decision functions or weighted threshold functions depending on the reliability of the sensor. For the first case, the sensor will return the value $z_{i}, z_{i}=[0 ; 1]$, which could be interpreted as either YES or NO. Then, the conditional probabilities $P\left(z_{i} \mid H_{0}\right)$ and $P\left(z_{i} \mid H_{1}\right)$ are defined by $F A R_{i}$ and $F R R_{i}$ values, respectively, for $i$ -th sensor. Here, $F A R_{i}$ and $F R R_{i}$ are taken at the CER/EER point, e . g ., $z_{i}$ is selected at the point where $F A R_{i}=F R R_{i}$. Generally, this methodology reflects the scenarios of ownership or knowledge factors from the biometric perspective.