TY - GEN
T1 - Context-aware deep learning-driven framework for mitigation of security risks in BYOD-enabled environments
AU - Petrov, Daniel
AU - Znati, Taieb
N1 - Funding Information:
This material is based upon work supported by the National Science Foundation under grant no. CNS-1642949 and grant no. CNS-1162159. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/11/15
Y1 - 2018/11/15
N2 - 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.
AB - 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.
KW - Access Control
KW - BOYD
KW - Information Security
KW - Machine Learning
KW - Mobile Devices
KW - Neural Networks.
KW - Security
UR - http://www.scopus.com/inward/record.url?scp=85059784354&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85059784354&partnerID=8YFLogxK
U2 - 10.1109/CIC.2018.00032
DO - 10.1109/CIC.2018.00032
M3 - Conference contribution
AN - SCOPUS:85059784354
T3 - Proceedings - 4th IEEE International Conference on Collaboration and Internet Computing, CIC 2018
SP - 166
EP - 175
BT - Proceedings - 4th IEEE International Conference on Collaboration and Internet Computing, CIC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE International Conference on Collaboration and Internet Computing, CIC 2018
Y2 - 18 October 2018 through 20 October 2018
ER -