Biblio

In this paper, an innovative approach to keyboard user monitoring (authentication), using keyboard dynamics and founded on the concept of time series analysis, is presented. The work is motivated by the need for robust authentication mechanisms in the context of on-line assessment such as those featured in many online learning platforms. Four analysis mechanisms are considered: analysis of keystroke time series in their raw form (without any translation), analysis consequent to translating the time series into a more compact form using either the Discrete Fourier Transform or the Discrete Wavelet Transform, and a "benchmark" feature vector representation of the form typically used in previous related work. All four mechanisms are fully described and evaluated. A best authentication accuracy of 99% was obtained using the wavelet transform.

The Internet of Things (IoT) is the latest evolution of the Internet, encompassing an enormous number of connected physical "things." The access-control oriented (ACO) architecture was recently proposed for cloud-enabled IoT, with virtual objects (VOs) and cloud services in the middle layers. A central aspect of ACO is to control communication among VOs. This paper develops operational and administrative access control models for this purpose, assuming topic-based publishsubscribe interaction among VOs. Operational models are developed using (i) access control lists for topics and capabilities for virtual objects and (ii) attribute-based access control, and it is argued that role-based access control is not suitable for this purpose. Administrative models for these two operational models are developed using (i) access control lists, (ii) role-based access control, and (iii) attribute-based access control. A use case illustrates the details of these access control models for VO communication, and their differences. An assessment of these models with respect to security and privacy preserving objectives of IoT is also provided.