# Biometrics for Internet-of-Things Security: A Review
## 6 Present Challenges
### Vulnerabilities of Biometric Systems and Their Impact on IoT Security  
The implementation of biometric systems in the IoT can hardly avoid security vulnerabilities that come with biometric authentication itself. The situation may be aggravated  
Figure 4: The enrollment and verification processes of (**a**) biometric key binding systems, and (**b**) biometric key generation systems (adapted from [93]). In the figure, ECC means error correction code.  
by the inherent characteristics of the IoT, such as limited resources and scalability, making biometric systems more vulnerable to certain attacks. There are various attacks targeting biometric authentication systems and [PERSON] et al. (2019) summarized eight different points of attacks, as shown in Figure 5. Among them, the attacks (e.g., spoofing attacks) to user interface at point (1) and the attacks (e.g., stealing biometric templates) to the biometric template database at point (6) are possibly the two most serious threats to IoT users' privacy ([PERSON] et al., 2017) and therefore discussed in detail in this work.  
At point (1), spoofing attacks to the user interface with fake biometric traits are common because biometric traits (e.g., face, fingerprint) are not secret, and adversaries can obtain them and use forged traits to spoof biometric systems. In the IoT environment, many IoT devices are unmanned and not equipped with liveness detection. For instance, the Touch ID of an iPhone can be fooled by a fake fingerprint film made from glue ([PERSON] et al., 2018). Since multi-modal biometric authentication systems are more robust than their unimodal counterparts ([PERSON] et al., 2018), incorporating multi-modal biometrics can mitigate the threat of spoofing attacks because it is more difficult to spoof two or more modalities than a single one ([PERSON] et al., 2018). However, multi-modal biometric systems are not the ideal remedy for spoofing attacks given the resource limitation of IoT devices. Using the biometric traits possessing the liveness property (e.g., ECG) can be a better option ([PERSON] et al., 2018), but it still depends on specific IoT applications because, in most applications, sensors may not be equipped with the ECG collection function.  
At point (6), attacks such as stealing or modifying biometric templates have severe consequences on users' privacy. This is because biometric template data are uniquely linked to users' identity and biometric traits cannot be revoked or reset similar to passwords or tokens. In the IoT context, with a large amount of sensitive data (including biometric data) collected and stored on IoT devices or servers, the privacy concern is more critical and security requirements are more demanding. Moreover, the IoT architecture is intended  
Figure 5: Eight possible attack points targeting biometric authentication systems. Two of them highlighted in the red circle are discussed in this work (adapted from ([PERSON] et al., 2017)).  
for automation without human intervention, making the task of protecting sensitive and important information (e.g., biometric templates) more challenging [98].