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Electronics
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Advanced Techniques in Computing and Security
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Advanced Techniques in Computing and Security

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 30459

Special Issue Editors

Prof. Dr. Jie Li

E-Mail Website
Guest Editor
School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200000, China
Interests: big data and AI; Blockchain; edge computing; networking and security; OS; information system architecture
Special Issues, Collections and Topics in MDPI journals
Dr. Xianfu Chen

E-Mail Website
Guest Editor
VTT Technical Research Centre of Finland, FI-90571 Oulu, Finland
Interests: radio resource management; heterogeneous wireless networks; game theory and machine learning in 5G networks and beyond
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Lei Shi

E-Mail Website
Guest Editor
School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou 450000, China
Interests: distributed computing; big data and artificial intelligence; network; information security
Special Issues, Collections and Topics in MDPI journals
Dr. Yangjie Cao

E-Mail Website
Guest Editor
School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou 450000, China
Interests: computer vision; intelligent computing; artificial intelligence; high-performance computing
Special Issues, Collections and Topics in MDPI journals
Dr. Bo Zhang

E-Mail Website
Guest Editor
School of Software, Zhengzhou University, Zhengzhou 450000, China
Interests: multimedia transmission; wireless live streaming; edge computing; multimedia mining; surveillance video processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

After many years of development, the techniques in computing and security have caused profound societal and financial effects. With the amount of data rapidly increasing, traditional methods cannot guarantee efficiency. Providing strong and effective methods to ensure the safety, efficiency, and security of their design and implementation is becoming urgent, both in the academy and industry. With the emergence of many applications, it is challenging for the current method to deal with the data effectively and safely. In particular, it is urgent to explore and exploit new technologies to collect, process, analyze, and apply such big data. Moreover, there are still many open problems in this area that need to be studied more deeply. Therefore, research on advanced techniques in computing and security can bring about countless potential improvements to our world.

The objective of this Special Issue is to attract the latest research results dedicated to computing and security. This Special Issue will bring leading researchers and developers from both academia and industry together to present their novel research on intelligent computing and cyber security. The submitted papers will be peer-reviewed and will be selected based on the quality and relevance to the main themes of this Special Issue.

Potential topics include, but are not limited to, the following:

  • Interconnection networks;
  • Sensor, wireless, and RFID systems;
  • Network-on-chip architectures;
  • Resource allocation and management;
  • Distributed computing;
  • Network routing and traffic control;
  • Operating systems for parallel/distributed systems;
  • Software engineering for parallel/distributed systems;
  • Generalized functional safety;
  • Cloud security;
  • Intrusion detection;
  • Data provenance; 
  • Cyberscience and engineering.

Prof. Dr. Jie Li
Dr. Xianfu Chen
Prof. Dr. Lei Shi
Dr. Yangjie Cao
Dr. Bo Zhang
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

  • computing
  • machine learning
  • sensing
  • networking
  • distributed systems
  • functional safety
  • cyber security

Related Special Issue

Published Papers (13 papers)

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Research

Jump to: Review

18 pages, 453 KiB  
Article
Efficient Medical Knowledge Graph Embedding: Leveraging Adaptive Hierarchical Transformers and Model Compression
by Xuexiang Li, Hansheng Yang, Cong Yang and Weixing Zhang
Electronics 2023, 12(10), 2315; https://doi.org/10.3390/electronics12102315 - 20 May 2023
Viewed by 1489
Abstract
Medical knowledge graphs have emerged as essential tools for representing complex relationships among medical entities. However, existing methods for learning embeddings from medical knowledge graphs, such as DistMult, RotatE, ConvE, InteractE, JointE, and ConvKB, may not adequately capture the unique challenges posed by [...] Read more.
Medical knowledge graphs have emerged as essential tools for representing complex relationships among medical entities. However, existing methods for learning embeddings from medical knowledge graphs, such as DistMult, RotatE, ConvE, InteractE, JointE, and ConvKB, may not adequately capture the unique challenges posed by the domain, including the heterogeneity of medical entities, rich hierarchical structures, large-scale, high-dimensionality, and noisy and incomplete data. In this study, we propose an Adaptive Hierarchical Transformer with Memory (AHTM) model, coupled with a teacher–student model compression approach, to effectively address these challenges and learn embeddings from a rich medical knowledge dataset containing diverse entities and relationship sets. We evaluate the AHTM model on this newly constructed “Med-Dis” dataset and demonstrate its superiority over baseline methods. The AHTM model achieves substantial improvements in Mean Rank (MR) and Hits@10 values, with the highest MR value increasing by nearly 56% and Hits@10 increasing by 39%. Furthermore, we observe similar performance enhancements on the “FB15K-237” and “WN18RR” datasets. Our model compression approach, incorporating knowledge distillation and weight quantization, effectively reduces the model’s storage and computational requirements, making it suitable for resource-constrained environments. Overall, the proposed AHTM model and compression techniques offer a novel and effective solution for learning embeddings from medical knowledge graphs and enhancing our understanding of complex relationships among medical entities, while addressing the inadequacies of existing approaches. Full article
(This article belongs to the Special Issue Advanced Techniques in Computing and Security)
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10 pages, 492 KiB  
Communication
ResE: A Fast and Efficient Neural Network-Based Method for Link Prediction
by Xuexiang Li, Hansheng Yang and Cong Yang
Electronics 2023, 12(8), 1919; https://doi.org/10.3390/electronics12081919 - 19 Apr 2023
Cited by 1 | Viewed by 857
Abstract
In this study, we present a novel embedding model, named ResE, for predicting links in knowledge graphs. ResE employs depth-separable convolution and residual blocks, integrated with channel attention mechanisms. ResE surpasses previously published models, including the closely related TransE model, by achieving the [...] Read more.
In this study, we present a novel embedding model, named ResE, for predicting links in knowledge graphs. ResE employs depth-separable convolution and residual blocks, integrated with channel attention mechanisms. ResE surpasses previously published models, including the closely related TransE model, by achieving the satisfactory mean rank (MR) and the excellent Hits@10 scores on both WN18RR and FB15K-237 benchmarks. ResE is a promising model for knowledge graph completion tasks, with potential for further investigation and extension to new applications such as user-oriented relationship modeling. Although comparatively shallow compared to computer vision convolutional architectures, future work may explore deeper convolutional models. ResE exhibits remarkable performance and outperforms existing approaches, thus setting a new benchmark for knowledge graph completion. The outcomes of our study illustrate the effectiveness of incorporating depth-separable convolution and residual blocks, accompanied by channel attention mechanisms, in modeling knowledge graphs. These findings highlight ResE’s potential to push the boundaries of cutting-edge in this domain. Full article
(This article belongs to the Special Issue Advanced Techniques in Computing and Security)
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17 pages, 3576 KiB  
Article
Review of Wafer Surface Defect Detection Methods
by Jianhong Ma, Tao Zhang, Cong Yang, Yangjie Cao, Lipeng Xie, Hui Tian and Xuexiang Li
Electronics 2023, 12(8), 1787; https://doi.org/10.3390/electronics12081787 - 10 Apr 2023
Cited by 4 | Viewed by 7135
Abstract
Wafer surface defect detection plays an important role in controlling product quality in semiconductor manufacturing, which has become a research hotspot in computer vision. However, the induction and summary of wafer defect detection methods in the existing review literature are not thorough enough [...] Read more.
Wafer surface defect detection plays an important role in controlling product quality in semiconductor manufacturing, which has become a research hotspot in computer vision. However, the induction and summary of wafer defect detection methods in the existing review literature are not thorough enough and lack an objective analysis and evaluation of the advantages and disadvantages of various techniques, which is not conducive to the development of this research field. This paper systematically analyzes the research progress of domestic and foreign scholars in the field of wafer surface defect detection in recent years. Firstly, we introduce the classification of wafer surface defect patterns and their causes. According to the different methods of feature extraction, the current mainstream methods are divided into three categories: the methods based on image signal processing, the methods based on machine learning, and the methods based on deep learning. Moreover, the core ideas of representative algorithms are briefly introduced. Then, the innovations of each method are compared and analyzed, and their limitations are discussed. Finally, we summarize the problems and challenges in the current wafer surface defect detection task, the future research trends in this field, and the new research ideas. Full article
(This article belongs to the Special Issue Advanced Techniques in Computing and Security)
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17 pages, 817 KiB  
Article
Query Join Order Optimization Method Based on Dynamic Double Deep Q-Network
by Lixia Ji, Runzhe Zhao, Yiping Dang, Junxiu Liu and Han Zhang
Electronics 2023, 12(6), 1504; https://doi.org/10.3390/electronics12061504 - 22 Mar 2023
Viewed by 1541
Abstract
A join order directly affects database query performance and computational overhead. Deep reinforcement learning (DRL) can explore efficient query plans while not exhausting the search space. However, the deep Q network (DQN) suffers from the overestimation of action values in query optimization, which [...] Read more.
A join order directly affects database query performance and computational overhead. Deep reinforcement learning (DRL) can explore efficient query plans while not exhausting the search space. However, the deep Q network (DQN) suffers from the overestimation of action values in query optimization, which can lead to limited query performance. In addition, ε-greedy exploration is not efficient enough and does not enable deep exploration. Accordingly, in this paper, we propose a dynamic double DQN (DDQN) order selection method(DDOS) for join order optimization. First, the method models the join query as a Markov decision process (MDP), then solves the DRL model by integrating the network model DQN and DDQN weighting into the DRL model’s estimation error problem in query joining, and finally improves the quality of developing query plans. And actions are selected using a dynamic progressive search strategy to improve the randomness and depth of exploration and accumulate a high information gain of exploration. The performance of the proposed method is compared with those of dynamic programming, heuristic algorithms, and DRL optimization methods based on the query set Join Order Benchmark (JOB). The experimental results show that the proposed method can effectively improve the query performance with a favorable generalization ability and robustness, and outperforms other baselines in multi-join query applications. Full article
(This article belongs to the Special Issue Advanced Techniques in Computing and Security)
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16 pages, 4668 KiB  
Article
An Air Pollutant Forecast Correction Model Based on Ensemble Learning Algorithm
by Jianhong Ma, Xiaoyan Ma, Cong Yang, Lipeng Xie, Weixing Zhang and Xuexiang Li
Electronics 2023, 12(6), 1463; https://doi.org/10.3390/electronics12061463 - 20 Mar 2023
Cited by 1 | Viewed by 1233
Abstract
In recent years, air pollutants have become an important issue in meteorological research and an indispensable part of air quality forecasting. To improve the accuracy of the Chinese Unified Atmospheric Chemistry Environment (CUACE) model’s air pollutant forecasts, this paper proposes a solution based [...] Read more.
In recent years, air pollutants have become an important issue in meteorological research and an indispensable part of air quality forecasting. To improve the accuracy of the Chinese Unified Atmospheric Chemistry Environment (CUACE) model’s air pollutant forecasts, this paper proposes a solution based on ensemble learning. Firstly, the forecast results of the CUACE model and the corresponding monitoring data are extracted. Then, using feature analysis, we screen the correction factors that affect air quality. The random forest algorithm, XGBoost algorithm, and GBDT algorithm are employed to correct the prediction results of PM2.5, PM10, and O3. To further optimize the model, we introduce the grid search method. Finally, we compare and analyze the correction effect and determine the best correction model for the three air pollutants. This approach enhances the precision of the CUACE model’s forecast and improves our understanding of the factors that affect air quality. The experimental results show that the model has a better prediction error correction effect than the traditional machine learning statistical model. After the algorithm correction, the prediction accuracy of PM2.5 and PM10 is increased by 60%, and the prediction accuracy of O3 is increased by 70%. Full article
(This article belongs to the Special Issue Advanced Techniques in Computing and Security)
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12 pages, 4661 KiB  
Communication
The Circular U-Net with Attention Gate for Image Splicing Forgery Detection
by Jin Peng, Yinghao Li, Chengming Liu and Xiaomeng Gao
Electronics 2023, 12(6), 1451; https://doi.org/10.3390/electronics12061451 - 19 Mar 2023
Cited by 3 | Viewed by 1327
Abstract
With the advent and rapid development of image tampering technology, it has become harmful to many aspects of our society. Thus, image tampering detection has been increasingly important. Although current forgery detection methods have achieved some success, the scale of the tampered areas [...] Read more.
With the advent and rapid development of image tampering technology, it has become harmful to many aspects of our society. Thus, image tampering detection has been increasingly important. Although current forgery detection methods have achieved some success, the scale of the tampered areas in each forgery image are different, and previous methods do not take this into account. In this paper, we believe that the inability of the network to accommodate tampered regions of various sizes is the main reason for the low precision. To address the mentioned problem, we propose a neural network architecture called CAU-Net, which adds residual propagation and feedback, attention gate and Atrous Spatial Pyramid Pooling with CBAM to the U-Net. The Atrous Spatial Pyramid Pooling with CBAM can capture information from multiple scales and adapt to differently sized target areas. In addition, CAU-Net can solve the vanishing gradient issue and suppress the weight of untampered regions, and CAU-Net is an end-to-end network without redundant image processing; thus, it is fast to detect suspicious images. In the end, we optimize the proposed network structure by ablation study, and the experimental results and visualization results demonstrate that our network has a better performance on CASIA and NIST16 compared with state of the art methods. Full article
(This article belongs to the Special Issue Advanced Techniques in Computing and Security)
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20 pages, 4296 KiB  
Article
Task Scheduling Based on Adaptive Priority Experience Replay on Cloud Platforms
by Cuixia Li, Wenlong Gao, Li Shi, Zhiquan Shang and Shuyan Zhang
Electronics 2023, 12(6), 1358; https://doi.org/10.3390/electronics12061358 - 12 Mar 2023
Viewed by 1516
Abstract
Task scheduling algorithms based on reinforce learning (RL) have been important methods with which to improve the performance of cloud platforms; however, due to the dynamics and complexity of the cloud environment, the action space has a very high dimension. This not only [...] Read more.
Task scheduling algorithms based on reinforce learning (RL) have been important methods with which to improve the performance of cloud platforms; however, due to the dynamics and complexity of the cloud environment, the action space has a very high dimension. This not only makes agent training difficult but also affects scheduling performance. In order to guide an agent’s behavior and reduce the number of episodes by using historical records, a task scheduling algorithm based on adaptive priority experience replay (APER) is proposed. APER uses performance metrics as scheduling and sampling optimization objectives with which to improve network accuracy. Combined with prioritized experience replay (PER), an agent can decide how to use experiences. Moreover, this algorithm also considers whether a subtask is executed in a workflow to improve scheduling efficiency. Experimental results on Tpc-h, Alibaba cluster data, and scientific workflows show that a model with APER has significant benefits in terms of convergence and performance. Full article
(This article belongs to the Special Issue Advanced Techniques in Computing and Security)
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19 pages, 3522 KiB  
Article
Multi-Attention-Based Semantic Segmentation Network for Land Cover Remote Sensing Images
by Jintong Jia, Jiarui Song, Qingqiang Kong, Huan Yang, Yunhe Teng and Xuan Song
Electronics 2023, 12(6), 1347; https://doi.org/10.3390/electronics12061347 - 12 Mar 2023
Cited by 3 | Viewed by 1510
Abstract
Semantic segmentation is a key technology for remote sensing image analysis widely used in land cover classification, natural disaster monitoring, and other fields. Unlike traditional image segmentation, there are various targets in remote sensing images, with a large feature difference between the targets. [...] Read more.
Semantic segmentation is a key technology for remote sensing image analysis widely used in land cover classification, natural disaster monitoring, and other fields. Unlike traditional image segmentation, there are various targets in remote sensing images, with a large feature difference between the targets. As a result, segmentation is more difficult, and the existing models retain low accuracy and inaccurate edge segmentation when used in remote sensing images. This paper proposes a multi-attention-based semantic segmentation network for remote sensing images in order to address these problems. Specifically, we choose UNet as the baseline model, using a coordinate attention-based residual network in the encoder to improve the extraction capability of the backbone network for fine-grained features. We use a content-aware reorganization module in the decoder to replace the traditional upsampling operator to improve the network information extraction capability, and, in addition, we propose a fused attention module for feature map fusion after upsampling, aiming to solve the multi-scale problem. We evaluate our proposed model on the WHDLD dataset and our self-labeled Lu County dataset. The model achieved an mIOU of 63.27% and 72.83%, and an mPA of 74.86% and 84.72%, respectively. Through comparison and confusion matrix analysis, our model outperformed commonly used benchmark models on both datasets. Full article
(This article belongs to the Special Issue Advanced Techniques in Computing and Security)
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9 pages, 1525 KiB  
Communication
Super-Resolution of Compressed Images Using Residual Information Distillation Network
by Yanqing Zhang, Jie Li, Nan Lin, Yangjie Cao and Cong Yang
Electronics 2023, 12(5), 1209; https://doi.org/10.3390/electronics12051209 - 03 Mar 2023
Viewed by 1251
Abstract
Super-Resolution (SR) is a fundamental computer vision task, which reconstructs high-resolution images from low-resolution ones. Existing SR methods mainly recover images from clear low-resolution images, leading to unsatisfactory results when processing compressed low-resolution images. In the paper, we propose a two-stage SR method [...] Read more.
Super-Resolution (SR) is a fundamental computer vision task, which reconstructs high-resolution images from low-resolution ones. Existing SR methods mainly recover images from clear low-resolution images, leading to unsatisfactory results when processing compressed low-resolution images. In the paper, we propose a two-stage SR method for compressed images, which consists of the Compression Artifact Removal Module (CARM) and Super-Resolution Module (SRM). The compressed low-resolution image is used to reconstruct the clear low-resolution image by CARM, and the high-resolution image is obtained by SRM. In addition, we propose a residual information distillation block to learn the texture details which are lost during the compression process. The proposed method has been validated and compared with the state of the art, and experimental results show that the proposed method outperforms other super-resolution methods in terms of visual effects and objective evaluation metrics. Full article
(This article belongs to the Special Issue Advanced Techniques in Computing and Security)
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15 pages, 1108 KiB  
Article
ADSAttack: An Adversarial Attack Algorithm via Searching Adversarial Distribution in Latent Space
by Haobo Wang, Chenxi Zhu, Yangjie Cao, Yan Zhuang, Jie Li and Xianfu Chen
Electronics 2023, 12(4), 816; https://doi.org/10.3390/electronics12040816 - 06 Feb 2023
Cited by 1 | Viewed by 1396
Abstract
Deep neural networks are susceptible to interference from deliberately crafted noise, which can lead to incorrect classification results. Existing approaches make less use of latent space information and conduct pixel-domain modification in the input space instead, which increases the computational cost and decreases [...] Read more.
Deep neural networks are susceptible to interference from deliberately crafted noise, which can lead to incorrect classification results. Existing approaches make less use of latent space information and conduct pixel-domain modification in the input space instead, which increases the computational cost and decreases the transferability. In this work, we propose an effective adversarial distribution searching-driven attack (ADSAttack) algorithm to generate adversarial examples against deep neural networks. ADSAttack introduces an affiliated network to search for potential distributions in image latent space for synthesizing adversarial examples. ADSAttack uses an edge-detection algorithm to locate low-level feature mapping in input space to sketch the minimum effective disturbed area. Experimental results demonstrate that ADSAttack achieves higher transferability, better imperceptible visualization, and faster generation speed compared to traditional algorithms. To generate 1000 adversarial examples, ADSAttack takes 11.08s and, on average, achieves a success rate of 98.01%. Full article
(This article belongs to the Special Issue Advanced Techniques in Computing and Security)
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21 pages, 6569 KiB  
Article
Zero-Trust Security Authentication Based on SPA and Endogenous Security Architecture
by Mingyang Xu, Junli Guo, Haoyu Yuan and Xinyu Yang
Electronics 2023, 12(4), 782; https://doi.org/10.3390/electronics12040782 - 04 Feb 2023
Viewed by 2282
Abstract
Zero-trust security architecture reconstructs the trust foundation of access control based on authentication and authorization by continuously authenticating the terminal during the authentication process and not relying solely on geographic location/user attributes as the sole basis for the trust assessment. However, due to [...] Read more.
Zero-trust security architecture reconstructs the trust foundation of access control based on authentication and authorization by continuously authenticating the terminal during the authentication process and not relying solely on geographic location/user attributes as the sole basis for the trust assessment. However, due to the fine-grained verification of identity under the zero-trust security architecture, there is a need for multiple authentication and authorization processes. If a single policy engine has unknown vulnerabilities and unknown backdoors to be maliciously attacked, or DDOS attacks initiated by known vulnerabilities cannot be prevented, the policy engine based on this control center architecture cannot meet the requirements of system security and reliability. Therefore, it is proposed to apply the SPA single-package authorization and endogenous security architecture to the zero-trust authentication system, which can realize the reliability, dynamism and diversity of system defense. Through the experimental antiattack analysis and antiattack test, the test from the proposed scheme found that when the system introduces the endogenous security architecture, the security of the system can be improved due to the complexity of the attack process and the increase in the cost of the attack. The test through both the security and system overhead found that the scheme can effectively improve the security of the system while ensuring the quality of network services, compared to the traditional scheme. It was found that the scheme can effectively improve the security of the system while ensuring the quality of network services and has better adaptability than the traditional zero-trust authentication scheme. Full article
(This article belongs to the Special Issue Advanced Techniques in Computing and Security)
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20 pages, 2439 KiB  
Article
Dynamic Data Integrity Auditing Based on Hierarchical Merkle Hash Tree in Cloud Storage
by Zhenpeng Liu, Shuo Wang, Sichen Duan, Lele Ren and Jianhang Wei
Electronics 2023, 12(3), 717; https://doi.org/10.3390/electronics12030717 - 01 Feb 2023
Cited by 1 | Viewed by 1685
Abstract
In cloud storage mode, users lose physical control over their data. To enhance the security of outsourced data, it is vital to audit the data integrity of the data owners. However, most of the current audit protocols have a single application scenario and [...] Read more.
In cloud storage mode, users lose physical control over their data. To enhance the security of outsourced data, it is vital to audit the data integrity of the data owners. However, most of the current audit protocols have a single application scenario and cannot accommodate the actual needs of individuals and enterprises. In this research, a safe and efficient auditing scheme is proposed that is based on a hierarchical Merkle tree. On the one hand, we use a hierarchical authentication data structure and local signature aggregation technique to reduce the scale of the Merkle tree. In addition, authoritative nodes are introduced to reduce the length of the authentication path and improve the update efficiency. On the other hand, we introduce a monitoring mechanism that is based on the original data integrity auditing model to analyze the cloud data, which improves the transparency and credibility of cloud service providers. In addition, we achieve incomplete data recovery through log analysis, which greatly reduces the number of replicas of files under the premise of multi-copy auditing, reduces the burden on cloud service providers, and improves the fairness of audit protocols. The theoretical analysis and experimental comparison prove that the method is secure and efficient. It can effectively reduce the computational overhead and storage overhead in integrity auditing. Full article
(This article belongs to the Special Issue Advanced Techniques in Computing and Security)
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Review

Jump to: Research

37 pages, 3454 KiB  
Review
Security and Privacy Issues in Software-Defined Networking (SDN): A Systematic Literature Review
by Muhammad Shoaib Farooq, Shamyla Riaz and Atif Alvi
Electronics 2023, 12(14), 3077; https://doi.org/10.3390/electronics12143077 - 14 Jul 2023
Cited by 2 | Viewed by 5413
Abstract
Software-defined network (SDNs) have fundamentally changed network infrastructure by decoupling the data plane and the control plane. This architectural shift rejuvenates the network layer by granting the re-programmability and centralized management of networks which brings about exciting challenges. Although an SDN seems to [...] Read more.
Software-defined network (SDNs) have fundamentally changed network infrastructure by decoupling the data plane and the control plane. This architectural shift rejuvenates the network layer by granting the re-programmability and centralized management of networks which brings about exciting challenges. Although an SDN seems to be a secured network when compared to conventional networks, it is still vulnerable and faces rigorous deployment challenges. Moreover, the bifurcation of data and control planes also opens up new security problems. This systematic literature review (SLR) has formalized the problem by identifying the potential attack scenarios and highlighting the possible vulnerabilities. Eighty-six articles have been selected carefully to formulize the SLR. In this SLR, we have identified major security attacks on SDN planes, including the application plane, control plane, and data plane. Moreover, this research also identifies the approaches used by industry experts and researchers to develop security solutions for SDN planes. In this research, we have introduced an attack taxonomy and proposed a collaborative security model after comprehensively identifying security attacks on SDN planes. Lastly, research gaps, challenges, and future directions are discussed for the deployment of secure SDNs. Full article
(This article belongs to the Special Issue Advanced Techniques in Computing and Security)
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