Abstract
In IoT based systems, authentication of users and devices is a major challenge where the traditional authentication mechanisms such as login-password are no longer supportable. It requires continuous authentication methods to ensure authenticity of the users and devices specifically for long sessions. The authentication through the IoT-Sensors based data has already gained the attention of a considerable number of researchers as it does not require direct involvement of the users and provide an extra layer of security and user privacy. Due to the instability of IoT-Sensor data, the authentication techniques need to extract a large number of features to produce high-accurate results. Moreover, the limited capabilities of computation, communication, storage, and the small battery power of IoT devices further makes its implementation hard. In this paper, we have introduced a user authentication framework for remote IoT users based on unique walking patterns to extract gait-related features with minimum data samples and cycle length. For an in-depth analysis of the distinctive features of the sensor-based gait profiles, we have applied an ECG signal processing technique. The proposed approach applies to a diverse range of IoT devices such as cellphones, smartwatches, and wearable sensors. The main contribution of the proposed approach is the deeper gait analysis with the least number of features and data for authentication purposes. We have introduced an authentication algorithm for feature comparison. The machine learning models have been applied to the gait based profiles for validation of the proposed approach. The detailed experimental analysis of different data sets has achieved an accuracy of 94% and an equal error rate (EER) of 6% higher than the existing approaches.