In this paper, we proposed a new authentication method for the Internet of Things IoT devices based on electroencephalography EEG signals, and hand gestures. The proposed EEG signals authentication method used low price NeuroSky MindWave headset and was based on choosing the adaptive thresholds of attention and meditation mode for the authentication key. Hand gestures to control authentication processes by using a general camera. To verify that a new authentication method is widely accepted, it must meet two main conditions security and usability. The evaluation of the prototype usability was based on ISO 9241-11:2018 standards usability model. Results revealed that our proposed demonstrated the ability of authentication by using EEG signals with the effectiveness of 92%, the efficiency of 93%, and User-Satisfaction is acceptable and satisfying. To assess evaluate the security of the prototype, we consider the most important three threats related to IoT devices are guessing, physical observation, and targeted impersonation. The results showed that the password strength, using the proposed system is stronger than that with a traditional keyboard, the proposed authentication method resistant to target impersonation and physical observation.

In this paper, a new natural human interaction authentication method has been proposed for the Internet of Things (IoT) devices. In this method the user draws doodles on-air for authentication. On-air drawing, refers to virtually drawing free hand-drawn doodles passwords through hand gestures on the air without touching anything which is recommended during COVID-19. This work uses Google Quick Draw doodles dataset for password doodles. The proposed method is based on a usual video camera, two lightweight Convolutional Neural Networks (CNN) and Kalman filter. The first CNN for hand gestures classification to overcome dynamic hand gestures challenges on the air. The second CNN for authentication verification. Kalman filter is used to correct and smooth the drawn line path on the air. To accept the new authentication method, it must achieve two main goals security and usability. The evaluation of the usability was based on ISO 9241-11:2018 standards usability model. The results revealed that the accuracy of the proposed authentication method is 95% and, the efficiency is 94% and user satisfaction is accepted. The evaluation of the security was based on two threats related to IoT devices which are guessing and physical observation. The results showed that the password strength of the proposed authentication method is stronger than the traditional 4-digits PIN password. The proposed authentication method is also resistant to physical observation threats.