Context-Aware Deep Learning-Driven Framework for Mitigation of Security Risks in BYOD-Enabled Environments

Abstract

The proliferation of smart phones and ubiquitous Internet access enable the emergence of BYOD (Bring Your Own Device) as an effective policy to increase efficiency and productivity in the workplace. The adoption of BYOD, however, gives rise to a number of security threats, including sensitive information infiltration and exfiltration, DoS attacks and privacy violation. This work proposes a framework to address precisely this issue. The main focus of the paper is on exploring the viability of BYOD in supporting collaboration among team members, in a heterogeneous mobile computing environments. The basic tenet of this work is to leverage artificial neural networks (ANN) and decision tree (DT) machine learning (ML) techniques to identify any attempts for access to sensitive information by nonlegitimate users and to facilitate the framework to baffle their access, in order to protect the data. The goal becomes even more challenging, incorporating the demands for low latency and high accuracy of the framework. The main contributions of the include the formulation of the BYOD unauthorized access control problem, a framework that uses ANN and DT ML techniques to detect anomalous behaviors and to identify unauthorized access to resources on BYOD devices. The proposed security techniques are implemented and evaluated, using a real dataset.