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Computers
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Software-Defined Internet of Everything
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Software-Defined Internet of Everything

A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "Internet of Things (IoT) and Industrial IoT".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 7794

Special Issue Editors

Dr. Guodong Wang

E-Mail Website
Guest Editor
Computer Science Department, Massachusetts College of Liberal Arts (MCLA), North Adams, MA, USA
Interests: software-defined networking (SDN); future smart grid architectures based on Internet of Things (IoT); cybersecurity in wireless sensor networks (WSN) and smart grids
Dr. Yanxiao Zhao

E-Mail Website
Guest Editor
Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, VA, USA
Interests: wireless networking; Internet of Things; cyber security

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) has revolutionized the way we interact with the world around us, connecting a vast array of devices and machines in ways that were once inconceivable. However, with the increasing complexity and scale of IoT networks, there is a growing need for more the intelligent and dynamic management of these interconnected devices.

The software-defined Internet of Everything (SDIoE) is an emerging paradigm that promises to address these challenges by applying software-defined networking (SDN) principles to the IoT. By leveraging the power of programmable networks and centralized control, SDIoE enables the more efficient and flexible management of IoT networks, leading to improved scalability, security, and performance.

This Special Issue invites authors to submit original research articles, reviews, and case studies that address the latest developments and challenges in the field of SDIoE. Topics of interest include, but are not limited to:

  • Software-defined networking for IoT;
  • Scalability and performance optimization in SDIoE;
  • Protocols in SDIoE;
  • Network management in SDIoE;
  • Security and privacy in SDIoE networks;
  • Use cases and case studies of SDIoE applications;
  • Integration of SDIoE with other emerging technologies such as edge computing and 5G networks;
  • Standards and interoperability for SDIoE;
  • Machine learning and AI in SDIoE;
  • Cloud-based SDIoE architecture and solutions.

Authors are encouraged to submit original research that presents novel contributions, as well as high-quality reviews and case studies that provide insights into the challenges and opportunities related to SDIoE. All submissions will be peer-reviewed and evaluated based on their originality, technical quality, and relevance to the field.

Dr. Guodong Wang
Dr. Yanxiao Zhao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computers is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • SDN
  • IoT
  • network management
  • machine learning
  • edge computing
  • cloud computing

Published Papers (4 papers)

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Research

22 pages, 1102 KiB  
Article
Smart Contract-Based Access Control Framework for Internet of Things Devices
by Md. Rahat Hasan, Ammar Alazab, Siddhartha Barman Joy, Mohammed Nasir Uddin, Md Ashraf Uddin, Ansam Khraisat, Iqbal Gondal, Wahida Ferdose Urmi and Md. Alamin Talukder
Computers 2023, 12(11), 240; https://doi.org/10.3390/computers12110240 - 20 Nov 2023
Cited by 1 | Viewed by 1902
Abstract
The Internet of Things (IoT) has recently attracted much interest from researchers due to its diverse IoT applications. However, IoT systems encounter additional security and privacy threats. Developing an efficient IoT system is challenging because of its sophisticated network topology. Effective access control [...] Read more.
The Internet of Things (IoT) has recently attracted much interest from researchers due to its diverse IoT applications. However, IoT systems encounter additional security and privacy threats. Developing an efficient IoT system is challenging because of its sophisticated network topology. Effective access control is required to ensure user privacy in the Internet of Things. Traditional access control methods are inappropriate for IoT systems because most conventional access control approaches are designed for centralized systems. This paper proposes a decentralized access control framework based on smart contracts with three parts: initialization, an access control protocol, and an inspection. Smart contracts are used in the proposed framework to store access control policies safely on the blockchain. The framework also penalizes users for attempting unauthorized access to the IoT resources. The smart contract was developed using Remix and deployed on the Ropsten Ethereum testnet. We analyze the performance of the smart contract-based access policies based on the gas consumption of blockchain transactions. Further, we analyze the system’s security, usability, scalability, and interoperability performance. Full article
(This article belongs to the Special Issue Software-Defined Internet of Everything)
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16 pages, 1791 KiB  
Article
Rapidrift: Elementary Techniques to Improve Machine Learning-Based Malware Detection
by Abishek Manikandaraja, Peter Aaby and Nikolaos Pitropakis
Computers 2023, 12(10), 195; https://doi.org/10.3390/computers12100195 - 28 Sep 2023
Viewed by 1367
Abstract
Artificial intelligence and machine learning have become a necessary part of modern living along with the increased adoption of new computational devices. Because machine learning and artificial intelligence can detect malware better than traditional signature detection, the development of new and novel malware [...] Read more.
Artificial intelligence and machine learning have become a necessary part of modern living along with the increased adoption of new computational devices. Because machine learning and artificial intelligence can detect malware better than traditional signature detection, the development of new and novel malware aiming to bypass detection has caused a challenge where models may experience concept drift. However, as new malware samples appear, the detection performance drops. Our work aims to discuss the performance degradation of machine learning-based malware detectors with time, also called concept drift. To achieve this goal, we develop a Python-based framework, namely Rapidrift, capable of analysing the concept drift at a more granular level. We also created two new malware datasets, TRITIUM and INFRENO, from different sources and threat profiles to conduct a deeper analysis of the concept drift problem. To test the effectiveness of Rapidrift, various fundamental methods that could reduce the effects of concept drift were experimentally explored. Full article
(This article belongs to the Special Issue Software-Defined Internet of Everything)
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25 pages, 4434 KiB  
Article
A Novel Dynamic Software-Defined Networking Approach to Neutralize Traffic Burst
by Aakanksha Sharma, Venki Balasubramanian and Joarder Kamruzzaman
Computers 2023, 12(7), 131; https://doi.org/10.3390/computers12070131 - 27 Jun 2023
Cited by 2 | Viewed by 1454
Abstract
Software-defined networks (SDN) has a holistic view of the network. It is highly suitable for handling dynamic loads in the traditional network with a minimal update in the network infrastructure. However, the standard SDN architecture control plane has been designed for single or [...] Read more.
Software-defined networks (SDN) has a holistic view of the network. It is highly suitable for handling dynamic loads in the traditional network with a minimal update in the network infrastructure. However, the standard SDN architecture control plane has been designed for single or multiple distributed SDN controllers facing severe bottleneck issues. Our initial research created a reference model for the traditional network, using the standard SDN (referred to as SDN hereafter) in a network simulator called NetSim. Based on the network traffic, the reference models consisted of light, modest and heavy networks depending on the number of connected IoT devices. Furthermore, a priority scheduling and congestion control algorithm is proposed in the standard SDN, named extended SDN (eSDN), which minimises congestion and performs better than the standard SDN. However, the enhancement was suitable only for the small-scale network because, in a large-scale network, the eSDN does not support dynamic SDN controller mapping. Often, the same SDN controller gets overloaded, leading to a single point of failure. Our literature review shows that most proposed solutions are based on static SDN controller deployment without considering flow fluctuations and traffic bursts that lead to a lack of load balancing among the SDN controllers in real-time, eventually increasing the network latency. Therefore, to maintain the Quality of Service (QoS) in the network, it becomes imperative for the static SDN controller to neutralise the on-the-fly traffic burst. Thus, our novel dynamic controller mapping algorithm with multiple-controller placement in the SDN is critical to solving the identified issues. In dSDN, the SDN controllers are mapped dynamically with the load fluctuation. If any SDN controller reaches its maximum threshold, the rest of the traffic will be diverted to another controller, significantly reducing delay and enhancing the overall performance. Our technique considers the latency and load fluctuation in the network and manages the situations where static mapping is ineffective in dealing with the dynamic flow variation. Full article
(This article belongs to the Special Issue Software-Defined Internet of Everything)
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17 pages, 2095 KiB  
Article
Machine Learning-Based Dynamic Attribute Selection Technique for DDoS Attack Classification in IoT Networks
by Subhan Ullah, Zahid Mahmood, Nabeel Ali, Tahir Ahmad and Attaullah Buriro
Computers 2023, 12(6), 115; https://doi.org/10.3390/computers12060115 - 29 May 2023
Cited by 5 | Viewed by 2329
Abstract
The exponential growth of the Internet of Things (IoT) has led to the rapid expansion of interconnected systems, which has also increased the vulnerability of IoT devices to security threats such as distributed denial-of-service (DDoS) attacks. In this paper, we propose a machine [...] Read more.
The exponential growth of the Internet of Things (IoT) has led to the rapid expansion of interconnected systems, which has also increased the vulnerability of IoT devices to security threats such as distributed denial-of-service (DDoS) attacks. In this paper, we propose a machine learning pipeline that specifically addresses the issue of DDoS attack detection in IoT networks. Our approach comprises of (i) a processing module to prepare the data for further analysis, (ii) a dynamic attribute selection module that selects the most adaptive and productive features and reduces the training time, and (iii) a classification module to detect DDoS attacks. We evaluate the effectiveness of our approach using the CICI-IDS-2018 dataset and five powerful yet simple machine learning classifiers—Decision Tree (DT), Gaussian Naive Bayes, Logistic Regression (LR), K-Nearest Neighbor (KNN), and Random Forest (RF). Our results demonstrate that DT outperforms its counterparts and achieves up to 99.98% accuracy in just 0.18 s of CPU time. Our approach is simple, lightweight, and accurate for detecting DDoS attacks in IoT networks. Full article
(This article belongs to the Special Issue Software-Defined Internet of Everything)
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