You Walk, We Authenticate: Lightweight Seamless Authentication Based on Gait in Wearable IoT Systems

Abstract

With a plethora of wearable IoT devices available today, we can easily monitor human activities, many of which are unconscious or subconscious. Interestingly, some of these activities exhibit distinct patterns for each individual, which can provide an opportunity to extract useful features for user authentication. Among those activities, walking is one of the most rudimentary and mundane activity. Considering each individual's unique walking pattern, gait, which is the pattern of limb movements during locomotion, can be utilized as a biometric feature for user authentication. In this paper, we propose a lightweight seamless authentication framework based on gait (LiSA-G) that can authenticate and identify users on the widely available commercial smartwatches. Unlike the existing works, our proposed framework extracts not only the statistical features but also the human-action-related features from the collected sensor data in order to more accurately and efficiently reveal distinct patterns. Our experimental results show that our framework achieves a higher authentication accuracy (i.e., an average equal error rate (EER) of 8.2%) in comparison with the existing works while requiring fewer features and less amount of sensor data. This makes our framework more practical and rapidly deployable in the wearable IoT systems with limited computing power and energy capacity.

Peer Review #1 of "A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches (v0.1)"

“…Authors claimed that this software-based solution can be integrated into any smartwatch with PPG sensors for multi-factor authentication. On the other hand, Musale et al (Musale et al 2019) proposed a lightweight authentication approach using gait biometric for authenticating users of commercial smart watches. This was realized by extracting the statistical features and person's actions-related features from the collected data of sensors, thereby improving both accuracy and efficiency.…”

Authentication of Remote IoT Users Based on Deeper Gait Analysis of Sensor Data

“…Recently, a gait based user authentication method is introduced by [22]. The authors have developed an android application for collecting data from smartwatches and cellphones and transfer it to the authentication server.…”

“…We also performed the experiments on the machine learningbased tools for validation of the proposed approach and comparison of the results. The random forest classifier performs better than other algorithms for activity and user Identification [21,22]. We select 10 gait cycle features of each subject for training and testing purposes of the ratio of 70 to 30.…”

“…For better readability and understanding, we only present experiments of only 10 subjects. For calculating the accuracy of each user, we apply the method used by [22].…”

Can a Smartband be Used for Continuous Implicit Authentication in Real Life

“…Acar et al [38] used smartwatches with keystroke dynamics for continuous authentication. Musale et al [25] proposed a continuous authentication system based on Motorola 360 Sport by using accelerometer and gyroscope features. Vhaduri and Poellabauer [22] proposed continuous user authentication scheme that uses 44 features extracted from various biometrics (calorie burn, metabolic equivalent of task (MET), heart rate and step count) using Fitbit Charge HR device and they achieved average accuracy of 87.37% with Quadratic SVM classifier in one-to-many approach and average accuracy of 93% with Quadratic SVM classifier in oneto-one approach.…”