24 research outputs found
Software Defined Network-Based Multi-Access Edge Framework for Vehicular Networks
The authors are grateful to the Deanship of Scientific Research at King Saud University for funding this work through Vice Deanship of Scientific Research Chairs: Chair of Pervasive and Mobile Computing.Peer reviewe
Software-Defined Network-Based Vehicular Networks: A Position Paper on Their Modeling and Implementation
There is a strong devotion in the automotive industry to be part of a wider
progression towards the Fifth Generation (5G) era. In-vehicle integration costs
between cellular and vehicle-to-vehicle networks using Dedicated Short Range
Communication could be avoided by adopting Cellular Vehicle-to-Everything
(C-V2X) technology with the possibility to re-use the existing mobile network
infrastructure. More and more, with the emergence of Software Defined Networks,
the flexibility and the programmability of the network have not only impacted
the design of new vehicular network architectures but also the implementation
of V2X services in future intelligent transportation systems. In this paper, we
define the concepts that help evaluate software-defined-based vehicular network
systems in the literature based on their modeling and implementation schemes.
We first overview the current studies available in the literature on C-V2X
technology in support of V2X applications. We then present the different
architectures and their underlying system models for LTE-V2X communications. We
later describe the key ideas of software-defined networks and their concepts
for V2X services. Lastly, we provide a comparative analysis of existing
SDN-based vehicular network system grouped according to their modeling and
simulation concepts. We provide a discussion and highlight vehicular ad-hoc
networks' challenges handled by SDN-based vehicular networks.Comment: 14 pages, 3 figures, Sensors 201
AEF : Adaptive en-route filtering to extend network lifetime in wireless sensor networks
Funding Information: This work was supported by National Research Foundation of Korea-Grant funded by the Korean Government (Ministry of Science and ICT)-NRF-2017R1A2B2012337). This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (Ministry of Science and ICT) NRF-2017R1A2B2012337). Publisher Copyright: © 2019 by the authors. Licensee MDPI, Basel, Switzerland.Peer reviewe
A Stacking-Based Deep Neural Network Approach for Effective Network Anomaly Detection
An anomaly-based intrusion detection system (A-IDS) provides a critical aspect in a modern computing infrastructure since new types of attacks can be discovered. It prevalently utilizes several machine learning algorithms (ML) for detecting and classifying network traffic. To date, lots of algorithms have been proposed to improve the detection performance of A-IDS, either using individual or ensemble learners. In particular, ensemble learners have shown remarkable performance over individual learners in many applications, including in cybersecurity domain. However, most existing works still suffer from unsatisfactory results due to improper ensemble design. The aim of this study is to emphasize the effectiveness of stacking ensemble-based model for A-IDS, where deep learning (e.g., deep neural network [DNN]) is used as base learner model. The effectiveness of the proposed model and base DNN model are benchmarked empirically in terms of several performance metrics, i.e., Matthew's correlation coefficient, accuracy, and false alarm rate. The results indicate that the proposed model is superior to the base DNN model as well as other existing ML algorithms found in the literature
Fog based Secure Framework for Personal Health Records Systems
The rapid development of personal health records (PHR) systems enables an
individual to collect, create, store and share his PHR to authorized entities.
Health care systems within the smart city environment require a patient to
share his PRH data with a multitude of institutions' repositories located in
the cloud. The cloud computing paradigm cannot meet such a massive
transformative healthcare systems due to drawbacks including network latency,
scalability and bandwidth. Fog computing relieves the burden of conventional
cloud computing by availing intermediate fog nodes between the end users and
the remote servers. Aiming at a massive demand of PHR data within a ubiquitous
smart city, we propose a secure and fog assisted framework for PHR systems to
address security, access control and privacy concerns. Built under a fog-based
architecture, the proposed framework makes use of efficient key exchange
protocol coupled with ciphertext attribute based encryption (CP-ABE) to
guarantee confidentiality and fine-grained access control within the system
respectively. We also make use of digital signature combined with CP-ABE to
ensure the system authentication and users privacy. We provide the analysis of
the proposed framework in terms of security and performance.Comment: 12 pages (CMC Journal, Tech Science Press
Auto-colorization of historical images using deep convolutional neural networks
Funding Information: Acknowledgments: This work was supported by the Research Center of College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia. The authors are grateful for this support.Peer reviewedPublisher PD
Medical Diagnostic Systems Using Artificial Intelligence (AI) Algorithms : Principles and Perspectives
Funding Information: This work was supported in part by the National Research Foundation of Korea grant funded by the Korean Government, Ministry of Science and ICT, under Grant NRF-2020R1A2B5B02002478, and in part by Sejong University through its Faculty Research Program.Peer reviewe