Facial reconstruction

Search LJMU Research Online

Browse Repository | Browse E-Theses

Intelligent Control and Security of Fog Resources in Healthcare Systems via a Cognitive Fog Model

Al-Khafajiy, M, Otoum, S, Baker, T, Asim, M, Maamar, Z, Aloqaily, M, Taylor, MJ and Randles, M Intelligent Control and Security of Fog Resources in Healthcare Systems via a Cognitive Fog Model. ACM Transactions on Internet Technology. ISSN 1533-5399 (Accepted)

[img]
Preview
Text
Cognitive_Fog_ACM_TOIT (1).pdf - Accepted Version

Download (1MB) | Preview

Abstract

There have been significant advances in the field of Internet of Things (IoT) recently, which have not always considered security or data security concerns: A high degree of security is required when considering the sharing of medical data over networks. In most IoT-based systems, especially those within smart-homes and smart-cities, there is a bridging point (fog computing) between a sensor network and the Internet which often just performs basic functions such as translating between the protocols used in the Internet and sensor networks, as well as small amounts of data processing. The fog nodes can have useful knowledge and potential for constructive security and control over both the sensor network and the data transmitted over the Internet. Smart healthcare services utilise such networks of IoT systems. It is therefore vital that medical data emanating from IoT systems is highly secure, to prevent fraudulent use, whilst maintaining quality of service providing assured, verified and complete data. In this paper, we examine the development of a Cognitive Fog (CF) model, for secure, smart healthcare services, that is able to make decisions such as opting-in and opting-out from running processes and invoking new processes when required, and providing security for the operational processes within the fog system. Overall, the proposed ensemble security model performed better in terms of Accuracy Rate, Detection Rate, and a lower False Positive Rate (standard intrusion detection measurements) than three base classifiers (K-NN, DBSCAN and DT) using a standard security dataset (NSL-KDD).

Item Type: Article
Uncontrolled Keywords: 0805 Distributed Computing, 0806 Information Systems, 0801 Artificial Intelligence and Image Processing
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Computer Science
Publisher: Association for Computing Machinery
Date Deposited: 10 Feb 2020 11:46
Last Modified: 10 Feb 2020 12:00
URI: http://researchonline.ljmu.ac.uk/id/eprint/12231

Actions (login required)

View Item View Item