ARTIFICIAL NEURAL NETWORK AND C4.5 ALGORITHMS FOR TAMPER DETECTION MODEL OF HEALTHCARE APPLICATIONS IN INTERNET OF THINGS | ||
| International Journal of Intelligent Computing and Information Sciences | ||
| Article 4, Volume 17, Issue 3, July 2017, Pages 51-63 | ||
| Document Type: Original Article | ||
| DOI: 10.21608/ijicis.2017.9148 | ||
| Authors | ||
| lobna yehia1; ashraf Darwish* 1; Ahmed elngar* 2; Ayman khedr* 3 | ||
| 1Computers Science Department Faculty of Science Helwan University, Cairo, Egypt | ||
| 2Computer Science Department, Al-Alson Higher Institute, Cairo, Egypt | ||
| 3Information Systems Department, Faculty of Computers & Information, Helwan University, Cairo, Egypt | ||
| Abstract | ||
| Abstract: Security of a network is the most important challenges of the Internet of Things (IOT) that needs smarter security mechanism. Tamper detection is an effective technique used to deal with security violations. In this paper, a new IoT-Tamper Detection Model (TDM) based IoT for real data of healthcare applications has been proposed. In this model, artificial neural network (ANN) algorithm with RC4-EA encryption method and IOT-C4.5 algorithm are applied for TDM. The experimental results showed that the detection performance of ANN is 98.51% and 76.66 % for the C4.5 algorithm. In addition, the proposed model showed that the ANN algorithm enhances the timing speed than C4.5 algorithm which is important for real time IOT - TDM healthcare application. | ||
| Keywords | ||
| Keywords: Internet of Things (IoT); Tamper detection; security; Healthcare applications; artificial neural network; C4.5 algorithm | ||
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