Next Article in Journal
CO2 Emissions Reduction and Energy Efficiency Improvements in Paper Making Drying Process Control by Sensors
Next Article in Special Issue
The PeRvasive Environment Sensing and Sharing Solution
Previous Article in Journal
Comparison of Organic and Integrated Nutrient Management Strategies for Reducing Soil N2O Emissions
Previous Article in Special Issue
Distributed Demand Side Management with Battery Storage for Smart Home Energy Scheduling
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle
Sustainability 2017, 9(4), 513; doi:10.3390/su9040513

SH-SecNet: An Enhanced Secure Network Architecture for the Diagnosis of Security Threats in a Smart Home

Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul 01811, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Sehyun Park
Received: 18 February 2017 / Revised: 21 March 2017 / Accepted: 27 March 2017 / Published: 28 March 2017
(This article belongs to the Special Issue The Advent of Smart Homes)
View Full-Text   |   Download PDF [2184 KB, uploaded 29 March 2017]   |  

Abstract

The growing demand for an independent and comfortable lifestyle has motivated the development of the smart home, and providing security is a major challenge for developers and security analysts. Enhancing security in the home environment has been recognized as one of the main obstacles to realizing the vision of creating energy-efficient smart homes and buildings. Understanding the risks associated with the use and potential exploitation of information about homes, end-users, and partners, as well as forming techniques for integrating security assessments into the design, is not straightforward. To address this challenge, we propose enhanced secure network architecture (SH-SecNet) for the diagnosis of security threats in the smart home. In our architecture, we use the Multivariate Correlation Analysis (MCA) technique to analyze the network flow packet in the network layer, as this classifies the network traffic by extracting the correlation between network traffic features. We evaluated the performance of our architecture with respect to various parameters, such as CPU utilization, throughput, round trip time, and accuracy. The result of the evaluation shows that our architecture is efficient and accurate in detecting and mitigating attacks in the smart home network with a low performance overhead. View Full-Text
Keywords: smart home; Internet-of-Things; security; threat diagnosis; Multivariate Correlation Analysis smart home; Internet-of-Things; security; threat diagnosis; Multivariate Correlation Analysis
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Singh, S.; Sharma, P.K.; Park, J.H. SH-SecNet: An Enhanced Secure Network Architecture for the Diagnosis of Security Threats in a Smart Home. Sustainability 2017, 9, 513.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sustainability EISSN 2071-1050 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top