Behavioural Authentication Based on Smartphone Protected Personal Communication Data

Smartphones have become ubiquitous in everyday life, storing and generating a huge amount of sensitive personal data which make them vulnerable to increasing security and privacy threats. While protecting smartphones has become a necessity, existing traditional authentication methods, which are mainly PINs and passwords, are facing remarkable drawbacks and behavioural biometrics-based authentication was adopted as the best alternative to ensure better protection. This paper presents a comparative study of many behavioural authentica-tion solutions using smartphone personal communication data. Different approaches are compared such as using Distance Minimization, K-means and Support Vector Machine (SVM) as classification method. The data privacy protection by using the BioHashing algorithm is also considered in the paper. The authentication approaches were tested on a dataset of 93 users with more than 16.000 samples and show promising results with an EER of 10% without any data protection with the One Class SVM method and an EER remarkably lower than 1% for the 3 adopted methods with data privacy protection.