Next Article in Journal
An Improved Multi-Sensor Fusion Navigation Algorithm Based on the Factor Graph
Next Article in Special Issue
Measuring Complexity and Predictability of Time Series with Flexible Multiscale Entropy for Sensor Networks
Previous Article in Journal
An Electricity Price-Aware Open-Source Smart Socket for the Internet of Energy
Previous Article in Special Issue
A Novel Topology Link-Controlling Approach for Active Defense of Nodes in Networks
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(3), 642; doi:10.3390/s17030642

A Quantitative Risk Assessment Model Involving Frequency and Threat Degree under Line-of-Business Services for Infrastructure of Emerging Sensor Networks

1
College of Information Engineering, Northwest A & F University, Yangling 712100, China
2
Department of Computing, The Hong Kong Polytechnic University, Hong Kong 999077, China
3
Department of Mathematics and Computer Science, Northeastern State University, Tahlequah, OK 74464, USA
4
College of Computer and Information Sciences, Almuzahmiyah, King Saud University, Riyadh 11451, Saudi Arabia
5
Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology, Lulea 97187, Sweden
*
Authors to whom correspondence should be addressed.
Academic Editor: Mohamed F. Younis
Received: 13 December 2016 / Revised: 16 March 2017 / Accepted: 16 March 2017 / Published: 21 March 2017
(This article belongs to the Special Issue Topology Control in Emerging Sensor Networks)
View Full-Text   |   Download PDF [3499 KB, uploaded 22 March 2017]   |  

Abstract

The prospect of Line-of-Business Services (LoBSs) for infrastructure of Emerging Sensor Networks (ESNs) is exciting. Access control remains a top challenge in this scenario as the service provider’s server contains a lot of valuable resources. LoBSs’ users are very diverse as they may come from a wide range of locations with vastly different characteristics. Cost of joining could be low and in many cases, intruders are eligible users conducting malicious actions. As a result, user access should be adjusted dynamically. Assessing LoBSs’ risk dynamically based on both frequency and threat degree of malicious operations is therefore necessary. In this paper, we proposed a Quantitative Risk Assessment Model (QRAM) involving frequency and threat degree based on value at risk. To quantify the threat degree as an elementary intrusion effort, we amend the influence coefficient of risk indexes in the network security situation assessment model. To quantify threat frequency as intrusion trace effort, we make use of multiple behavior information fusion. Under the influence of intrusion trace, we adapt the historical simulation method of value at risk to dynamically access LoBSs’ risk. Simulation based on existing data is used to select appropriate parameters for QRAM. Our simulation results show that the duration influence on elementary intrusion effort is reasonable when the normalized parameter is 1000. Likewise, the time window of intrusion trace and the weight between objective risk and subjective risk can be set to 10 s and 0.5, respectively. While our focus is to develop QRAM for assessing the risk of LoBSs for infrastructure of ESNs dynamically involving frequency and threat degree, we believe it is also appropriate for other scenarios in cloud computing. View Full-Text
Keywords: cloud computing; line-of-business services; access control; risk assessment; intrusion effort cloud computing; line-of-business services; access control; risk assessment; intrusion effort
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

Jing, X.; Hu, H.; Yang, H.; Au, M.H.; Li, S.; Xiong, N.; Imran, M.; Vasilakos, A.V. A Quantitative Risk Assessment Model Involving Frequency and Threat Degree under Line-of-Business Services for Infrastructure of Emerging Sensor Networks. Sensors 2017, 17, 642.

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]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top