# Personalized Context-Aware Authentication Protocols in IoT
## 2 Materials and Methods  
As has been reviewed in preliminary studies, the methods that have already been used and proven/disproven to work fall into three main categories (with prominent examples below):  
* Biometrics;  
These include fingerprints, retinal patterns, and iris recognition [4; 5]. All of these are based on the proven fact of the uniqueness of the characteristics being checked [6]. Retina checks have not proven to be usable in the IoT world, mainly due to the invasiveness of the procedure and the need for specialized scanners [7]. However, fingerprint sensors are currently widely used in the majority of more sophisticated devices (smartphones), and iris recognition, although more complicated, can still be achieved using modern cameras,given a properly lit environment [8]. Both of those methods require access to sensitive data and dedicated but readily available sensors. * Neuroscience patterns;  
These include subconsciously imprinted movements (trainable reflexes) or emotions (mental states). Both, although usable in IoT, have proven to be too specialized or challenging to use to be widespread. The first method requires a lot of training for the subjects as well as requires constant repetition to be usable [9], and the second one requires too much specialized equipment (EEG (electroencephalograph) scanners or electrodermal activity measuring equipment) and still can only be used in conjunction with another method, as it does not guarantee the uniqueness of a subject [10]. * Personal characteristics and habits. These include typing cadence, eye movements, and sitting posture (with ECG (electrocardiograph) tracking). The latter, although tested and used in automated cars to secure the ignition system and recognize the driver, still requires a lot of specialized sensors and might be too cumbersome for widespread usage in the general IoT environment [11]. On the other hand, both typing cadence (during password or PIN checking) [12] and eye movement tracking have proven to be helpful, with a minimal surcharge of added computations to the standard authentication methods. Of these two methods, eye movement tracking can be particularly interesting, as it is based on reflexes that are trained with everyday usage and could be based on pattern/picture recognition by an authenticated subject [13]. Based on these categories [14] and the authors' work [15], a fourth category was proposed:  
* Cognitive cryptography. Due to the above methods, with some exceptions being too cumbersome or invasive, there is a need for methods that would need no more specialized equipment than what is already provided and could take advantage of the internal characteristics of an individual (which makes it closest to category three above). The main difference would be the awareness of the context of the authenticated person (whereabouts, daily routine, prerecorded habits, etc.) and the usage of the sensors that are available with the devices present on hand (be it a fingerprint reader, camera, a personal NAS (network-attached storage), a GPS (global positioning system), or a personal fitness band). All of these can be used to provide a digital footprint of their owner and authenticate based on already provided information, even if the data can be incomplete (due to a lack of proper sensors, bandwidth, processing power, or even WAN (wide area network) connectivity) [16]. The results of the comparative study can be seen below (Table 1) [15].