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A Deep Learning Approach Combining Autoencoder with One-class SVM for DDoS Attack Detection in SDNs

Lotfi Mhamdi, Desmond McLernon, Fadi El-moussa, Syed Ali Raza Zaidi, Mounir Ghogho, Tuan Tang
<span title="2020-10-27">2020</span> <i title="IEEE"> 2020 IEEE Eighth International Conference on Communications and Networking (ComNet) </i> &nbsp;
In this paper, we propose a hybrid unsupervised DL approach using the stack autoencoder and One-class Support Vector Machine (SAE-1SVM) for Distributed Denial of Service (DDoS) attack detection.  ...  Recently, several Machine Learning (ML)/Deep Learning (DL) intrusion detection approaches have been proposed to secure SDN networks.  ...  In this paper, we propose an unsupervised hybrid 978-1-7281-5320-9/20/$31.00 © 2020 IEEE approach combining Stack Autoencoder with OC-SVM (SAE-1SVM) for DDoS attack detection in the SDN. B.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/comnet47917.2020.9306073">doi:10.1109/comnet47917.2020.9306073</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lsdz6jgf2faznah4ns3462q3ii">fatcat:lsdz6jgf2faznah4ns3462q3ii</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210428035657/http://eprints.whiterose.ac.uk/157807/1/Comnet%20paper.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/6e/8b/6e8b68cfd93f591ab393a77bc6b2a16b8383f192.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/comnet47917.2020.9306073"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

DDoSNet: A Deep-Learning Model for Detecting Network Attacks [article]

Mahmoud Said Elsayed, Nhien-An Le-Khac, Soumyabrata Dev, Anca Delia Jurcut
<span title="2020-06-24">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Our method is based on Deep Learning (DL) technique, combining the Recurrent Neural Network (RNN) with autoencoder.  ...  The current methods deploy Machine Learning (ML) for intrusion detection against DDoS attacks in the SDN network using the standard datasets.  ...  The contribution of this paper includes the following: • We leverage and propose a deep learning approach based on RNN-autoencoder for detection of DDoS attacks on the SDN (DDoSNet).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2006.13981v1">arXiv:2006.13981v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lskgm7zq4bbo7p56kdxsgvr7fm">fatcat:lskgm7zq4bbo7p56kdxsgvr7fm</a> </span>
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A Deep Learning Based DDoS Detection System in Software-Defined Networking (SDN)

Quamar Niyaz, Weiqing Sun, Ahmad Y. Javaid
<span title="2017-12-28">2017</span> <i title="European Alliance for Innovation n.o."> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sfw6e4bmgrh6rkjkiiddq77y3m" style="color: black;">EAI Endorsed Transactions on Security and Safety</a> </i> &nbsp;
We propose a deep learning based multi-vector DDoS detection system in a software-defined network (SDN) environment.  ...  We observe high accuracy with a low false-positive for attack detection in our proposed system.  ...  Conclusion In this work, we implemented a deep learning based DDoS detection system for multi-vector attack detection in an SDN environment.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.4108/eai.28-12-2017.153515">doi:10.4108/eai.28-12-2017.153515</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/m57bijlh6rbexhh6cz5vdfzmaq">fatcat:m57bijlh6rbexhh6cz5vdfzmaq</a> </span>
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Intelligent Traffic Management in Next-Generation Networks

Ons Aouedi, Kandaraj Piamrat, Benoît Parrein
<span title="2022-01-28">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/hijy7jexkvcipg3tulqv73bck4" style="color: black;">Future Internet</a> </i> &nbsp;
We start by presenting a comprehensive background beginning from conventional ML algorithms and DL and follow this with a focus on different dimensionality reduction techniques.  ...  The research community has advocated the application of ML/DL in softwarized environments for network traffic management, including traffic classification, prediction, and anomaly detection.  ...  Therefore, detection and mitigation of DDoS attacks in real-time is necessary. As a result, several ML models have been used for DDoS attack detection in the SDN environment.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/fi14020044">doi:10.3390/fi14020044</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dx6leecgtfegdlao5aq6p6ouci">fatcat:dx6leecgtfegdlao5aq6p6ouci</a> </span>
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An Evolutionary SVM Model for DDOS Attack Detection in Software Defined Networks

Kshira Sagar Sahoo, Bata Krishna Tripathy, K.S. Naik, Somula Ramasubbareddy, Balamurugan Balusamy, Manju Khari, Daniel Burgos
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
Nowadays, in the field of SDN, various machine learning (ML) techniques are being deployed for detecting malicious traffic.  ...  For better detection accuracy, in this work, Support Vector Machine (SVM) is assisted by kernel principal component analysis (KPCA) with genetic algorithm (GA).  ...  TABLE 1 . 1 Existing DDoS attack detection techniques used in SDN. et al. use Deep Learning-based Sparse Autoencoder (SAE) for their malicious defense system.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.3009733">doi:10.1109/access.2020.3009733</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/okcpxhlsqfdyxc4lenq4fwth4u">fatcat:okcpxhlsqfdyxc4lenq4fwth4u</a> </span>
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Machine Learning Approach Equipped with Neighbourhood Component Analysis for DDoS Attack Detection in Software-Defined Networking

Özgür Tonkal, Hüseyin Polat, Erdal Başaran, Zafer Cömert, Ramazan Kocaoğlu
<span title="">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ikdpfme5h5egvnwtvvtjrnntyy" style="color: black;">Electronics</a> </i> &nbsp;
We handle a public "DDoS attack SDN Dataset" including a total of 23 features.  ...  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).  ...  [10] introduced a new evolutionary model to classify DDoS attack traffic in an SDN environment. The model uses a combined SVM algorithm for malicious traffic classification.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/electronics10111227">doi:10.3390/electronics10111227</a> <a target="_blank" rel="external noopener" href="https://doaj.org/article/fa503bb1cf974f72be91e4bb37d48dd9">doaj:fa503bb1cf974f72be91e4bb37d48dd9</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/obenjsgwbvb3rd4cfo7za4n5dq">fatcat:obenjsgwbvb3rd4cfo7za4n5dq</a> </span>
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SDN-Enabled Hybrid DL-Driven Framework for the Detection of Emerging Cyber Threats in IoT

Danish Javeed, Tianhan Gao, Muhammad Taimoor Khan
<span title="2021-04-12">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ikdpfme5h5egvnwtvvtjrnntyy" style="color: black;">Electronics</a> </i> &nbsp;
We present an SDN-enabled architecture leveraging hybrid deep learning detection algorithms for the efficient detection of cyber threats and attacks while considering the resource-constrained IoT devices  ...  However, the centralized intelligence and programmability of software-defined networks (SDNs) have made it possible to compose a single and effective security solution to cope with cyber threats and attacks  ...  CNN and LSTM-based detection systems are used to detect adversarial attacks in SDNs [32] . The deep learning approach has proved to have a great potential for the detection of malevolent activities.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/electronics10080918">doi:10.3390/electronics10080918</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rj4h2wx3zvfedfp5ygzxhyxiya">fatcat:rj4h2wx3zvfedfp5ygzxhyxiya</a> </span>
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Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments

Ahamed Aljuhani
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
INDEX TERMS DDoS attacks and detection, Internet of Things (IoT), machine learning (ML), network functions virtualization (NFV), software-defined network (SDN).  ...  In recent years, machine learning (ML) techniques have been widely used to prevent DDoS attacks.  ...  [15] proposed a deep learning framework for DDoS attack detection in the context of an SDN.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3062909">doi:10.1109/access.2021.3062909</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xtj576lfsffrbpiqyk2kv5wuam">fatcat:xtj576lfsffrbpiqyk2kv5wuam</a> </span>
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Comparison of machine learning algorithms for DDoS attack detection in SDN

Duc Le, Minh Dao, Quyen Nguyen
<span title="2020-06-15">2020</span> <i title="State University of Aerospace Instrumentation (SUAI)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/zhxd67kb4jgfvm3yshkqwnqahq" style="color: black;">Information and Control Systems</a> </i> &nbsp;
The algorithms more suitable for machine learning can help us to detect DDoS attacks in software-defined networks more accurately.  ...  One of the most recent solutions to detect a DDoS attack is using machine learning algorithms to classify the traffic.  ...  A DDoS detection system that incorporates stacked autoencoder based deep learning approach in an SDN environment was implemented.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.31799/1684-8853-2020-3-59-70">doi:10.31799/1684-8853-2020-3-59-70</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fmbq2h2usjhspotynrh3ccyojq">fatcat:fmbq2h2usjhspotynrh3ccyojq</a> </span>
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Deep Recurrent Neural Network for Intrusion Detection in SDN-based Networks

Tuan A Tang, Lotfi Mhamdi, Des McLernon, Syed Ali Raza Zaidi, Mounir Ghogho
<span title="">2018</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wem7gsbe7rh5hdvk376bnrenl4" style="color: black;">2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft)</a> </i> &nbsp;
Through extensive experiments, we conclude that the proposed approach exhibits a strong potential for intrusion detection in the SDN environments.  ...  Consequently, in this paper, we propose a Gated Recurrent Unit Recurrent Neural Network (GRU-RNN) enabled intrusion detection systems for SDNs.  ...  Their detection rate is 96% with just first 250 packets. In [17] , the authors propose a DL based approach using a stacked autoencoder (SAE) for detecting DDoS attacks in the SDN.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/netsoft.2018.8460090">doi:10.1109/netsoft.2018.8460090</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/netsoft/TangMMZG18.html">dblp:conf/netsoft/TangMMZG18</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ulzayccfjfd6xbzdp3ze23swka">fatcat:ulzayccfjfd6xbzdp3ze23swka</a> </span>
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DeepIDS: Deep Learning Approach for Intrusion Detection in Software Defined Networking

Tuan Anh Tang, Lotfi Mhamdi, Des McLernon, Syed Ali Raza Zaidi, Mounir Ghogho, Fadi El Moussa
<span title="">2020</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ikdpfme5h5egvnwtvvtjrnntyy" style="color: black;">Electronics</a> </i> &nbsp;
In this paper, we propose a deep learning (DL) approach for a network intrusion detection system (DeepIDS) in the SDN architecture.  ...  Through experiments, we confirm that the DL approach has the potential for flow-based anomaly detection in the SDN environment.  ...  One-class SVM was trained with a malicious dataset for a low false alarm rate in [19] . AlEroud et al.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/electronics9091533">doi:10.3390/electronics9091533</a> <a target="_blank" rel="external noopener" href="https://doaj.org/article/f9e1c3d429e54c9b85cf3eb12fe5cd03">doaj:f9e1c3d429e54c9b85cf3eb12fe5cd03</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mbeay5gl7jb27cdst6ls7dl7hi">fatcat:mbeay5gl7jb27cdst6ls7dl7hi</a> </span>
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Deep Semisupervised Learning-Based Network Anomaly Detection in Heterogeneous Information Systems

Nazarii Lutsiv, Taras Maksymyuk, Mykola Beshley, Orest Lavriv, Volodymyr Andrushchak, Anatoliy Sachenko, Liberios Vokorokos, Juraj Gazda
<span title="">2022</span> <i title="Computers, Materials and Continua (Tech Science Press)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/amujz7fcqna6do727z6ev3ueo4" style="color: black;">Computers Materials &amp; Continua</a> </i> &nbsp;
The proposed approach is based on a deep recurrent autoencoder that learns time series of normal network behavior and detects notable network anomalies.  ...  In this paper, we propose a novel intrusion detection system (IDS) based on deep learning that aims to identify suspicious behavior in modern heterogeneous information systems.  ...  Acknowledgement: The authors are thankful to the administration of the Lviv Polytechnic National University and the Technical University of Kosice for providing the necessary equipment to conduct this  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.32604/cmc.2022.018773">doi:10.32604/cmc.2022.018773</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/itxfgifm5rgrhiqpqqjc5ckfmu">fatcat:itxfgifm5rgrhiqpqqjc5ckfmu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220225182616/https://www.techscience.com/ueditor/files/cmc/TSP_CMC_70-1/TSP_CMC_18773/TSP_CMC_18773.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c9/95/c99545bddffbc5ac560bdc86115b003a98c93909.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.32604/cmc.2022.018773"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Analysis of Different Attacks on Software Defined Network and Approaches to Mitigate using Intelligent Techniques

P. Karthika, A. Karmel
<span title="">2021</span> <i title="The Science and Information Organization"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2yzw5hsmlfa6bkafwsibbudu64" style="color: black;">International Journal of Advanced Computer Science and Applications</a> </i> &nbsp;
An overview of deep learning algorithms for sensing distributed denial of service attacks in softwaredefined networks with Deep learning is presented within this article.  ...  Both Software Defined Networking (SDN) and Deep Learning (DL) have recently found a number of practical and fascinating applications in industry and academia.  ...  Hameed et al [29] developed a combined way for defending SDN against DDoS attacks.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14569/ijacsa.2021.0120938">doi:10.14569/ijacsa.2021.0120938</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/r5cm4po2rnashlmu54kz2bhpgu">fatcat:r5cm4po2rnashlmu54kz2bhpgu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211011093221/https://thesai.org/Downloads/Volume12No9/Paper_38-Analysis_of_Different_Attacks_on_Software_Defined_Network.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/bc/1b/bc1bc5b0be976379a0fae673c7a33f530c1206c7.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14569/ijacsa.2021.0120938"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Security Analysis of DDoS Attacks Using Machine Learning Algorithms in Networks Traffic

Rami J. Alzahrani, Ahmed Alzahrani
<span title="2021-11-25">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ikdpfme5h5egvnwtvvtjrnntyy" style="color: black;">Electronics</a> </i> &nbsp;
In this research, the current studies in the use of deep learning (DL) in DDoS intrusion detection have been presented.  ...  This research aims to implement different Machine Learning (ML) algorithms in WEKA tools to analyze the detection performance for DDoS attacks using the most recent CICDDoS2019 datasets.  ...  I would like to thank him for his expert advice and usual support.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/electronics10232919">doi:10.3390/electronics10232919</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/do4rzyjcofdqvo27barezqbtaa">fatcat:do4rzyjcofdqvo27barezqbtaa</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220323023515/https://mdpi-res.com/d_attachment/electronics/electronics-10-02919/article_deploy/electronics-10-02919.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/aa/61/aa61850f64c0c433b69a4308575f898c0356159f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/electronics10232919"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

Deep Learning-Based Intrusion Detection Systems: A Systematic Review

Jan Lansky, Saqib Ali, Mokhtar Mohammadi, Mohammed Kamal Majeed, Sarkhel H. Taher Karim, Shima Rashidi, Mehdi Hosseinzadeh, Amir Masoud Rahmani
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
This survey article focuses on the deep learning-based intrusion detection schemes and puts forward an in-depth survey and classification of these schemes.  ...  Deep learning is one of the exciting techniques which recently are vastly employed by the IDS or intrusion detection systems to increase their performance in securing the computer networks and hosts.  ...  In [88] , Niyaz et al. presented a multi-vector DDoS attack detection system based on deep learning for the SDN and implemented it on the SDN controllers as an application.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3097247">doi:10.1109/access.2021.3097247</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/un54rxgjyvfx3pxberzpafioxm">fatcat:un54rxgjyvfx3pxberzpafioxm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210804110417/https://ieeexplore.ieee.org/ielx7/6287639/9312710/09483916.pdf?tp=&amp;arnumber=9483916&amp;isnumber=9312710&amp;ref=" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e3/10/e310e1a774e44ec7e2ccf45bac1ac0f4df0213ab.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3097247"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>
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