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Artificial Intelligence Driven Software-Defined Networking (SDN) Technologies for Next Generation Networks

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 29142

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Special Issue Editors

Dr. Houbing Song

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Guest Editor

Department of Electrical Engineering and Computer Science, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USA
Interests: AI/machine learning; cyber-physical systems; cybersecurity and privacy; unmanned aircraft systems; communications and networking
Special Issues, Collections and Topics in MDPI journals

Dr. Jehad Ali

E-Mail Website
Guest Editor

Department of AI Convergence Network, Ajou University, Suwon 16499, Republic of Korea
Interests: software-defined networking; quality of service; WSN; IoT; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

SDN dynamically and efficiently manages resources to provide diverse services by leveraging controller intelligence and programmability. SDN allows network systems to orchestrate and estimate available resources, as well as dynamically adapt to the environment to maximize resource utilization.

As a promising solution, artificial intelligence is becoming a successful way to boost the intelligence of SDN controllers. Machine learning and artificial intelligence (AI) techniques are effective for network communication adaptation. The controller, which has been trained with sophisticated AI and machine learning algorithms, can improve the provision of end-to-end (E2E) services, security, network slicing, and resource management.

This Special Issue anticipates cutting-edge SDN technologies based on AI/machine learning techniques, covering new research findings with a diverse range of elements within intelligent SDN technology for next-generation networks. Potential topics include but are not limited to the following:

Leveraging AI/SDN/NFV for network slicing in 5G and Beyond Networks;
AI/SDN enabled resource and mobility management in 5G and Beyond Networks;
AI or machine learning-based software-defined networks;
AI for energy-efficient software-defined networks;
Load balancing in energy constrained environments using AI/SDN;
AI for robust control plan in SDN;
Controller placement problem optimization in SDN using AI;
AI/SDN for ultra-reliable low latency communication (URLL);
High security and privacy with AI/SDN;
Efficient fault recovery leveraging AI/SDN;
Application of software-defined networks/AI in Fog computing, UAV, resource management, and edge computing;
AI/SDN for mission-critical applications.

Prof. Dr. Houbing Song
Dr. Jehad Ali
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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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

artificial intelligence
machine learning
SDN/NFV
virtual network functions (VNF’s)
tactile internet
5G/6G
URLL

Published Papers (11 papers)

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Research

Jump to: Review

17 pages, 2733 KiB

Open AccessArticle

RSLSP: An Effective Recovery Scheme for Flash Memory Leveraging Shadow Paging

by Abdulhadi Alahmadi and Tae Sun Chung

Electronics 2022, 11(24), 4126; https://doi.org/10.3390/electronics11244126 - 10 Dec 2022

Cited by 2 | Viewed by 1139

Abstract

The flash storage is a non-volatile semiconductor device that is constantly powered and has several advantages such as small size, lower power consumption, fast access, convenient portability, heat dissipation, shock resistance, data retention next to a power off, and random access. Flash memory is presently being incorporated with distinct embedded system devices such as with digital cameras, smart phones, personal digital assistants (PDA), and sensor devices. Nevertheless, a flash memory entails special features such as “erase-before-write” and “wear-leveling”, an FTL (flash translation layer) upon the software layer should be included. Although, the power off recovery plays a significant role in portable devices, most FTL algorithms did not consider the power off recovery scheme. In this paper, we proposed an effective scheme for the recovery of flash memory leveraging the shadow paging concept for storage devices using flash memory. To combat the sudden power off problem, the suggested RSLSP approach saves and keeps the map block data as a combination of two tables, i.e., first is the original block and the second block is a replica for the original one. Our proposed strategy not only improves the capacity of a flash memory device as compared to the state-of-the-art schemes suggested in the literature, but is also compatible with the existing FTL-based schemes. Full article

(This article belongs to the Special Issue Artificial Intelligence Driven Software-Defined Networking (SDN) Technologies for Next Generation Networks)

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17 pages, 19699 KiB

Open AccessArticle

Research on Generalized Intelligent Routing Technology Based on Graph Neural Network

by Xiangyu Zheng, Wanwei Huang, Hui Li and Guangyuan Li

Electronics 2022, 11(18), 2952; https://doi.org/10.3390/electronics11182952 - 17 Sep 2022

Cited by 4 | Viewed by 2452

Abstract

Aiming at the problems of poor load balancing ability and weak generalization of the existing routing algorithms, this paper proposes an intelligent routing algorithm, GNN-DRL, in the Software Defined Networking (SDN) environment. The GNN-DRL algorithm uses a graph neural network (GNN) to perceive the dynamically changing network topology, generalizes the state of nodes and edges, and combines the self-learning ability of Deep Reinforcement Learning (DRL) to find the optimal routing strategy, which makes GNN-DRL minimize the maximum link utilization and reduces average end-to-end delay under high network load. In this paper, the GNN-DRL intelligent routing algorithm is compared with the Open Shortest Path First (OSPF), Equal-Cost Multi-Path (ECMP), and intelligence-driven experiential network architecture for automatic routing (EARS). The experimental results show that GNN-DRL reduces the maximum link utilization by 13.92% and end-to-end delay by 9.48% compared with the superior intelligent routing algorithm EARS under high traffic load, and can be effectively extended to different network topologies, making possible better load balancing capability and generalizability. Full article

(This article belongs to the Special Issue Artificial Intelligence Driven Software-Defined Networking (SDN) Technologies for Next Generation Networks)

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17 pages, 6813 KiB

Open AccessArticle

Implementation of a Clustering-Based LDDoS Detection Method

by Tariq Hussain, Muhammad Irfan Saeed, Irfan Ullah Khan, Nida Aslam and Sumayh S. Aljameel

Electronics 2022, 11(18), 2804; https://doi.org/10.3390/electronics11182804 - 06 Sep 2022

Cited by 2 | Viewed by 1279

Abstract

With the rapid advancement and transformation of technology, information and communication technologies (ICT), in particular, have attracted everyone’s attention. The attackers took advantage of this and can caused serious problems, such as malware attack, ransomware, SQL injection attack, etc. One of the dominant attacks, known as distributed denial-of-service (DDoS), has been observed as the main reason for information hacking. In this paper, we have proposed a secure technique, called the low-rate distributed denial-of-service (LDDoS) technique, to measure attack penetration and secure communication flow. A two-step clustering method was adopted, where the network traffic was controlled by using the characteristics of TCP traffic with discrete sense; then, the suspicious cluster with the abnormal analysis was detected. This method has proven to be reliable and efficient for LDDoS attacks detection, based on the NS-2 simulator, compared to the exponentially weighted moving average (EWMA) technique, which has comparatively very high false-positive rates. Analyzing abnormal test pieces helps us reduce the false positives. The proposed methodology was implemented using Python for scripting and NS-2 simulator for topology, two public trademark datasets, i.e., Web of Information for Development (WIDE) and Lawrence Berkley National Laboratory (LBNL), were selected for experiments. The experiments were analyzed, and the results evaluated using Wireshark. The proposed LDDoS approach achieved good results, compared to the previous techniques. Full article

(This article belongs to the Special Issue Artificial Intelligence Driven Software-Defined Networking (SDN) Technologies for Next Generation Networks)

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19 pages, 8793 KiB

Open AccessArticle

Research on Energy-Saving Routing Technology Based on Deep Reinforcement Learning

by Xiangyu Zheng, Wanwei Huang, Sunan Wang, Jianwei Zhang and Huanlong Zhang

Electronics 2022, 11(13), 2035; https://doi.org/10.3390/electronics11132035 - 29 Jun 2022

Cited by 2 | Viewed by 1413

Abstract

With the vigorous development of the Internet, the network traffic of data centers has exploded, and at the same time, the network energy consumption of data centers has also increased rapidly. Existing routing algorithms only realize routing optimization through Quality of Service (QoS) and Quality of Experience (QoE), which ignores the energy consumption of data center networks. Aiming at this problem, this paper proposes an Ee-Routing algorithm, which is an energy-saving routing algorithm based on deep reinforcement learning. First, our method takes the energy consumption and network performance of the data plane in the software-defined network as the joint optimization goal and establishes an energy-efficient traffic scheduling scheme for the elephant flows and the mice flows. Then, we use Deep Deterministic Policy Gradient (DDPG), which is a deep learning framework, to achieve continuous and energy-efficient traffic scheduling for joint optimization goals. The training process of our method is based on a Convolutional Neural Network (CNN), which can effectively improve the convergence efficiency of the algorithm. After the algorithm training converges, the energy-efficient path weights of the elephant flows and the mice flows are output, and the balanced scheduling of routing energy-saving and network performance is completed. Finally, the results show that our algorithm has good convergence and stability. Compared with the DQN-EER routing algorithm, Ee-Routing improves the energy saving percentage by 13.93%, and compared with the EARS routing algorithm, Ee-Routing reduces the delay by 13.73%, increases the throughput by 10.91%, and reduces the packet loss rate by 13.51%. Full article

(This article belongs to the Special Issue Artificial Intelligence Driven Software-Defined Networking (SDN) Technologies for Next Generation Networks)

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Graphical abstract

24 pages, 3741 KiB

Open AccessFeature PaperArticle

Lightweight Challenge-Response Authentication in SDN-Based UAVs Using Elliptic Curve Cryptography

by Muhammad Usman, Rashid Amin, Hamza Aldabbas and Bader Alouffi

Electronics 2022, 11(7), 1026; https://doi.org/10.3390/electronics11071026 - 25 Mar 2022

Cited by 16 | Viewed by 2944

Abstract

Unmanned aerial vehicles (UAVs) (also known as drones) are aircraft that do not require the presence of a human pilot to fly. UAVs can be controlled remotely by a human operator or autonomously by onboard computer systems. UAVs have many military uses, including battlefield surveillance, effective target tracking and engagement in air-to-ground warfare, and situational awareness in challenging circumstances. They also offer a distinct advantage in various applications such as forest fire monitoring and surveillance. Surveillance systems are developed using advanced technologies in the modern era of communications and networks. As a result, UAVs require enhancements to control and manage systems efficiently. Network security is a critical concern with respect to UAVs due to the risk of surveillance information theft and physical misuse. Although several new tools have been introduced to secure networks, attackers can use more advanced methods to get into a UAV network and create problems that pose an organizational threat to the entire system. Security mechanisms also reduce the performance of systems because some restrictive measures prevent users from accessing specific resources, but a few techniques and tools have overcome the problem of performance reduction in various scenarios. There are many types of attacks, i.e., denial of service attacks (DOS), distributed denial of service attacks (DDOS), address resolution protocol (ARP) spoofing, sniffing, etc., that make it challenging to maintain a UAV network. This research paper proposes a lightweight challenge-response authentication that can overcome the previously mentioned problems. As security is provided by utilizing a minimum number of bits in memory, this technique offers the same security features while using fewer network resources, low computing resources, and low power consumption. Full article

(This article belongs to the Special Issue Artificial Intelligence Driven Software-Defined Networking (SDN) Technologies for Next Generation Networks)

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16 pages, 919 KiB

Open AccessArticle

Routing Algorithms Simulation for Self-Aware SDN

by Mateusz P. Nowak and Piotr Pecka

Electronics 2022, 11(1), 104; https://doi.org/10.3390/electronics11010104 - 29 Dec 2021

Cited by 3 | Viewed by 1547

Abstract

This paper presents a self-aware network approach with cognitive packets, with a routing engine based on random neural networks. The simulation study, performed using a custom simulator extension of OmNeT++, compares RNN routing with other routing methods. The performance results of RNN-based routing, combined with the distributed nature of its operation inaccessible to other presented methods, demonstrate the advantages of introducing neural networks as a decision-making mechanism in selecting network paths. This work also confirms the usefulness of the simulator for SDN networks with cognitive packets and various routing algorithms, including RNN-based routing engines. Full article

(This article belongs to the Special Issue Artificial Intelligence Driven Software-Defined Networking (SDN) Technologies for Next Generation Networks)

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16 pages, 4175 KiB

Open AccessArticle

Wild Animal Information Collection Based on Depthwise Separable Convolution in Software Defined IoT Networks

by Qinghua Cao, Lisu Yu, Zhen Wang, Shanjun Zhan, Hao Quan, Yan Yu, Zahid Khan and Anis Koubaa

Electronics 2021, 10(17), 2091; https://doi.org/10.3390/electronics10172091 - 28 Aug 2021

Cited by 3 | Viewed by 1969

Abstract

The wild animal information collection based on the wireless sensor network (WSN) has an enormous number of applications, as demonstrated in the literature. Yet, it has many problems, such as low information density and high energy consumption ratio. The traditional Internet of Things (IoT) system has characteristics of limited resources and task specificity. Therefore, we introduce an improved deep neural network (DNN) structure to solve task specificity. In addition, we determine a programmability idea of software-defined network (SDN) to solve the problems of high energy consumption ratio and low information density brought about by low autonomy of equipment. By introducing some advanced network structures, such as attention mechanism, residuals, depthwise (DW) convolution, pointwise (PW) convolution, spatial pyramid pooling (SPP), and feature pyramid networks (FPN), a lightweight object detection network with a fast response is designed. Meanwhile, the concept of control plane and data plane in SDN is introduced, and nodes are divided into different types to facilitate intelligent wake-up, thereby realizing high-precision detection and high information density of the detection system. The results show that the proposed scheme can improve the detection response speed and reduce the model parameters while ensuring detection accuracy in the software-defined IoT networks. Full article

(This article belongs to the Special Issue Artificial Intelligence Driven Software-Defined Networking (SDN) Technologies for Next Generation Networks)

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25 pages, 15410 KiB

Open AccessArticle

Differential Evolution Based Machine Learning Scheme for Secure Cooperative Spectrum Sensing System

by Noor Gul, Su Min Kim, Saeed Ahmed, Muhammad Sajjad Khan and Junsu Kim

Electronics 2021, 10(14), 1687; https://doi.org/10.3390/electronics10141687 - 14 Jul 2021

Cited by 7 | Viewed by 1920

Abstract

The secondary users (SUs) in cognitive radio networks (CRNs) can obtain reliable spectrum sensing information of the primary user (PU) channel using cooperative spectrum sensing (CSS). Multiple SUs share their sensing observations in the CSS system to tackle fading and shadowing conditions. The presence of malicious users (MUs) may pose threats to the performance of CSS due to the reporting of falsified sensing data to the fusion center (FC). Different categories of MUs, such as always yes, always no, always opposite, and random opposite, are widely investigated by researchers. To this end, this paper proposes a hybrid boosted tree algorithm (HBTA)-based solution that combines the differential evolution (DE) and boosted tree algorithm (BTA) to mitigate the effects of MUs in the CSS systems, leading to reliable sensing results. An optimized threshold and coefficient vector, determined against the SUs employing DE, is utilized to train the BTA. The BTA is a robust ensembling machine learning (ML) technique gaining attention in spectrum sensing operations. To show the effectiveness of the proposed scheme, extensive simulations are performed at different levels of signal-to-noise-ratios (SNRs) and with different sensing samples, iteration levels, and population sizes. The simulation results show that more reliable spectrum decisions can be achieved compared to the individual utilization of DE and BTA schemes. Furthermore, the obtained results show the minimum sensing error to be exhibited by the proposed HBTA employing a DE-based solution to train the BTA. Additionally, the proposed scheme is compared with several other CSS schemes such as simple DE, simple BTA, maximum gain combination (MGC), particle swarm optimization (PSO), genetic algorithm (GA), and K-nearest neighbor (KNN) algorithm-based soft decision fusion (SDF) schemes to validate its effectiveness. Full article

(This article belongs to the Special Issue Artificial Intelligence Driven Software-Defined Networking (SDN) Technologies for Next Generation Networks)

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16 pages, 1299 KiB

Open AccessArticle

Machine Learning Approach Equipped with Neighbourhood Component Analysis for DDoS Attack Detection in Software-Defined Networking

by Özgür Tonkal, Hüseyin Polat, Erdal Başaran, Zafer Cömert and Ramazan Kocaoğlu

Electronics 2021, 10(11), 1227; https://doi.org/10.3390/electronics10111227 - 21 May 2021

Cited by 62 | Viewed by 6294

Abstract

The Software-Defined Network (SDN) is a new network paradigm that promises more dynamic and efficiently manageable network architecture for new-generation networks. With its programmable central controller approach, network operators can easily manage and control the whole network. However, at the same time, due to its centralized structure, it is the target of many attack vectors. Distributed Denial of Service (DDoS) attacks are the most effective attack vector to the SDN. The purpose of this study is to classify the SDN traffic as normal or attack traffic using machine learning algorithms equipped with Neighbourhood Component Analysis (NCA). We handle a public “DDoS attack SDN Dataset” including a total of 23 features. The dataset consists of Transmission Control Protocol (TCP), User Datagram Protocol (UDP), and Internet Control Message Protocol (ICMP) normal and attack traffics. The dataset, including more than 100 thousand recordings, has statistical features such as byte_count, duration_sec, packet rate, and packet per flow, except for features that define source and target machines. We use the NCA algorithm to reveal the most relevant features by feature selection and perform an effective classification. After preprocessing and feature selection stages, the obtained dataset was classified by k-Nearest Neighbor (kNN), Decision Tree (DT), Artificial Neural Network (ANN), and Support Vector Machine (SVM) algorithms. The experimental results show that DT has a better accuracy rate than the other algorithms with 100% classification achievement. Full article

(This article belongs to the Special Issue Artificial Intelligence Driven Software-Defined Networking (SDN) Technologies for Next Generation Networks)

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Review

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21 pages, 1688 KiB

Open AccessReview

Crash Recovery Techniques for Flash Storage Devices Leveraging Flash Translation Layer: A Review

by Abdulhadi Alahmadi and Tae Sun Chung

Electronics 2023, 12(6), 1422; https://doi.org/10.3390/electronics12061422 - 16 Mar 2023

Cited by 2 | Viewed by 1837

Abstract

The flash storage is a type of nonvolatile semiconductor device that is operated continuously and has been substituting the hard disk or secondary memory in several storage markets, such as PC/laptop computers, mobile devices, and is also used as an enterprise server. Moreover, it offers a number of benefits, including compact size, low power consumption, quick access, easy mobility, heat dissipation, shock tolerance, data preservation during a power outage, and random access. Different embedded system products, including digital cameras, smartphones, personal digital assistants (PDA), along with sensor devices, are currently integrating flash memory. However, as flash memory requires unique capabilities such as “erase before write” as well as “wear-leveling”, a FTL (flash translation layer) is added to the software layer. The FTL software module overcomes the problem of performance that arises from the erase before write operation and wear-leveling, i.e., flash memory does not allow for an in-place update, and therefore a block must be erased prior to overwriting upon the present data. In the meantime, flash storage devices face challenges of failure and thus they must be able to recover metadata (as well as address mapping information), including data after a crash. The FTL layer is responsible for and intended for use in crash recovery. Although the power-off recovery technique is essential for portable devices, most FTL algorithms do not take this into account. In this paper, we review various schemes of crash recovery leveraging FTL for flash storage devices. We illustrate the classification of the FTL algorithms. Moreover, we also discuss the various metrics and parameters evaluated for comparison with other approaches by each scheme, along with the flash type. In addition, we made an analysis of the FTL schemes. We also describe meaningful considerations which play a critical role in the design development for power-off recovery employing FTL. Full article

(This article belongs to the Special Issue Artificial Intelligence Driven Software-Defined Networking (SDN) Technologies for Next Generation Networks)

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19 pages, 3083 KiB

Open AccessReview

A Survey on MAC-Based Physical Layer Security over Wireless Sensor Network

by Attique Ur Rehman, Muhammad Sajid Mahmood, Shoaib Zafar, Muhammad Ahsan Raza, Fahad Qaswar, Sumayh S. Aljameel, Irfan Ullah Khan and Nida Aslam

Electronics 2022, 11(16), 2529; https://doi.org/10.3390/electronics11162529 - 12 Aug 2022

Cited by 3 | Viewed by 2629

Abstract

Physical layer security for wireless sensor networks (WSNs) is a laborious and highly critical issue in the world. Wireless sensor networks have great importance in civil and military fields or applications. Security of data/information through wireless medium remains a challenge. The data that we transmit wirelessly has increased the speed of transmission rate. In physical layer security, the data transfer between source and destination is not confidential, and thus the user has privacy issues, which is why improving the security of wireless sensor networks is a prime concern. The loss of physical security causes a great threat to a network. We have various techniques to resolve these issues, such as interference, noise, fading in the communications, etc. In this paper we have surveyed the different parameters of a security design model to highlight the vulnerabilities. Further we have discussed the various attacks on different layers of the TCP/IP model along with their mitigation techniques. We also elaborated on the applications of WSNs in healthcare, military information integration, oil and gas. Finally, we have proposed a solution to enhance the security of WSNs by adopting the alpha method and handshake mechanism with encryption and decryption. Full article

(This article belongs to the Special Issue Artificial Intelligence Driven Software-Defined Networking (SDN) Technologies for Next Generation Networks)

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