Multifactor Authentication

2. State-of-the-Art and Potential MFA Sources

2.2.1. Behavior Detection

Back in time, behavior recognition was utilized to analyze military telegraph operator's typing rhythm to track the movement of the troops. Today, gestures for authentication purposes may range from conventional to "hard-to-mimic" ones, since motor-programmed skill results in the movement being organized before the actual execution.

A modern example of such identification is the process of tapping the smartphone screen. This approach could be easily combined with any text-input authentication methods as a typing pattern is unique for each person. In case the MFA system is specifically developed for predefined gesture analysis, the user is required to replicate a previously learned movement while holding or wearing the sensing device.

A natural step of authentication for widely used handheld and wearable devices is the utilization of accelerometer fingerprinting. For instance, each smartphone holder could be verified based on the gait pattern by continuously monitoring the accelerometer data that is almost impossible to fake by another individual .

For in-vehicle authentication, the integral system is expected to monitor the driver-specific features, which could be analyzed from two perspectives: vehicle-specific behavior: steering angle sensor, speed sensor, brake pressure sensor, etc.; and human factors: music played, calls made, presence of people in the car, etc.. Another important blocker-factor is alcohol sensor. The engine start function could be blocked in case when the level of alcohol in the cabin is above an acceptable legal limit.