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Автор: Grafiati

Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Stretched Deep Networks (SDN)"

Uhongora, Uakomba, Ronald Mulinde, Yee Wei Law, and Jill Slay. "Deep-learning-based Intrusion Detection for Software-defined Networking Space Systems." European Conference on Cyber Warfare and Security 22, no. 1 (June 19, 2023): 639–47. http://dx.doi.org/10.34190/eccws.22.1.1085.

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This paper briefly reviews the application of the Software-defined Networking (SDN) architecture to satellite networks. It highlights the prominent cyber threats that SDN-based satellite networks are vulnerable to and proposes relevant defence mechanisms. SDN transforms traditional networking architectures by separating the control plane from the forwarding (data) plane. This separation enhances scalability and centralises management. In comparison, in traditional networks, the control plane and the data plane are usually combined, resulting in complex network management and reduced scalability. Satellite networks can take advantage of these benefits offered by SDN and this supports them as key enablers of critical services, including weather prediction, global broadband Internet coverage, and Internet of Things (IoT) services. Ease of configuration and flexibility are essential for satellites providing critical services to instantly adapt to network changes. These desirable attributes can be realised by applying SDN to satellite networks. Although SDN offers significant benefits to satellite networks, it is vulnerable to cyber-attacks and particularly due to its centralised architecture. A common attack on SDN is the Distributed Denial of Service (DDoS) attack which could render the entire SDN unavailable. To mitigate such threats, an efficient Intrusion Detection System (IDS) is required to monitor the network and detect any suspicious traffic. However, traditional IDSs produce too many false positives and often fail to detect advanced attacks. For their ability to learn feature hierarchies in network traffic data automatically, whether, for network traffic classification or anomaly detection, deep learning (DL) plays an increasingly important role in IDSs. In this paper, we present a brief review of recent developments in cyber security for SDN-based space systems, and we identify vulnerabilities and threats to an SDN-based satellite network. We further discuss the potential of a DL-based IDS for the detection of cyber threats. Finally, we identify further research gaps in the recent literature and propose future research directions.

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Yaser, Ahmed Latif, Hamdy M. Mousa, and Mahmoud Hussein. "Improved DDoS Detection Utilizing Deep Neural Networks and Feedforward Neural Networks as Autoencoder." Future Internet 14, no. 8 (August 12, 2022): 240. http://dx.doi.org/10.3390/fi14080240.

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Software-defined networking (SDN) is an innovative network paradigm, offering substantial control of network operation through a network’s architecture. SDN is an ideal platform for implementing projects involving distributed applications, security solutions, and decentralized network administration in a multitenant data center environment due to its programmability. As its usage rapidly expands, network security threats are becoming more frequent, leading SDN security to be of significant concern. Machine-learning (ML) techniques for intrusion detection of DDoS attacks in SDN networks utilize standard datasets and fail to cover all classification aspects, resulting in under-coverage of attack diversity. This paper proposes a hybrid technique to recognize denial-of-service (DDoS) attacks that combine deep learning and feedforward neural networks as autoencoders. Two datasets were analyzed for the training and testing model, first statically and then iteratively. The auto-encoding model is constructed by stacking the input layer and hidden layer of self-encoding models’ layer by layer, with each self-encoding model using a hidden layer. To evaluate our model, we use a three-part data split (train, test, and validate) rather than the common two-part split (train and test). The resulting proposed model achieved a higher accuracy for the static dataset, where for ISCX-IDS-2012 dataset, accuracy reached a high of 99.35% in training, 99.3% in validation and 99.99% in precision, recall, and F1-score. for the UNSW2018 dataset, the accuracy reached a high of 99.95% in training, 0.99.94% in validation, and 99.99% in precision, recall, and F1-score. In addition, the model achieved great results with a dynamic dataset (using an emulator), reaching a high of 97.68% in accuracy.

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Hande, Yogita, and Akkalashmi Muddana. "A Survey on Intrusion Detection System for Software Defined Networks (SDN)." International Journal of Business Data Communications and Networking 16, no. 1 (January 2020): 28–47. http://dx.doi.org/10.4018/ijbdcn.2020010103.

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Presently, the advances of the internet towards a wide-spread growth and the static nature of traditional networks has limited capacity to cope with organizational business needs. The new network architecture software defined networking (SDN) appeared to address these challenges and provides distinctive features. However, these programmable and centralized approaches of SDN face new security challenges which demand innovative security mechanisms like intrusion detection systems (IDS's). The IDS of SDN are designed currently with a machine learning approach; however, a deep learning approach is also being explored to achieve better efficiency and accuracy. In this article, an overview of the SDN with its security concern and IDS as a security solution is explained. A survey of existing security solutions designed to secure the SDN, and a comparative study of various IDS approaches based on a deep learning model and machine learning methods are discussed in the article. Finally, we describe future directions for SDN security.

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Shen, Fan, and Levi Perigo. "Dynamic SDN Controller Placement based on Deep Reinforcement Learning." International Journal of Next-Generation Networks 15, no. 1 (March 30, 2023): 1–13. http://dx.doi.org/10.5121/ijngn.2023.15101.

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Software-defined Networking (SDN) is a revolutionary network architecture whose benefits stem partly from separating the data plane and control plane. In this scheme, the control functionalities are relocated to a logically centralized SDN controller which makes efficient and globally optimal forwarding decisions for network devices. Despite the fact that network virtualization technologies enable elastic capacity engineering and seamless fault recovery of the SDN controller, an optimal controller placement strategy that can adapt to changes in networks is an important but underexplored research topic. This paper proposes a novel deep reinforcement learning-based model that dynamically and strategically adjusts the location of the controller to minimize the OpenFlow latency in a virtualized environment. The experimental results demonstrate that the proposed strategy out performs both a random strategy and a generic strategy. Furthermore, this paper provides detailed instructions on how to implement the proposed model in realworld software-defined networks.

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Zhang, Tianyi, and Yong Wang. "RLFAT: A Transformer-Based Relay Link Forged Attack Detection Mechanism in SDN." Electronics 12, no. 10 (May 15, 2023): 2247. http://dx.doi.org/10.3390/electronics12102247.

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SDN is a modern internet architecture that has transformed the traditional internet structure in recent years. By segregating the control and data planes of the network, SDN facilitates centralized management, scalability, dynamism, and programmability. However, this very feature makes SDN controllers vulnerable to cyber attacks, which can cause network-wide crashes, unlike conventional networks. One of the most stealthy attacks that SDN controllers face is the relay link forgery attack in topology deception attacks. Such an attack can result in erroneous overall views for SDN controllers, leading to network functionality breakdowns and even crashes. In this article, we introduce the Relay Link Forgery Attack detection model based on the Transformer deep learning model for the first time. The model (RLFAT) detects relay link forgery attacks by extracting features from network flows received by SDN controllers. A dataset of network flows received by SDN controllers from a large number of SDN networks with different topologies was collected. Finally, the Relay-based Link Forgery Attack detection model was trained on this dataset, and its performance was evaluated using accuracy, recall, F1 score, and AUC metrics. For better validation, comparative experiments were conducted with some common deep learning models. The experimental results show that our proposed model (RLFAT) has good performance in detecting RLFA and outperforms other models.

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Li, Jinlong, Xiaochen Yuan, Jinfeng Li, Guoheng Huang, Ping Li, and Li Feng. "CD-SDN: Unsupervised Sensitivity Disparity Networks for Hyper-Spectral Image Change Detection." Remote Sensing 14, no. 19 (September 26, 2022): 4806. http://dx.doi.org/10.3390/rs14194806.

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Deep neural networks (DNNs) could be affected by the regression level of learning frameworks and challenging changes caused by external factors; their deep expressiveness is greatly restricted. Inspired by the fine-tuned DNNs with sensitivity disparity to the pixels of two states, in this paper, we propose a novel change detection scheme served by sensitivity disparity networks (CD-SDN). The CD-SDN is proposed for detecting changes in bi-temporal hyper-spectral images captured by the AVIRIS sensor and HYPERION sensor over time. In the CD-SDN, two deep learning frameworks, unchanged sensitivity network (USNet) and changed sensitivity network (CSNet), are utilized as the dominant part for the generation of binary argument map (BAM) and high assurance map (HAM). Then two approaches, arithmetic mean and argument learning, are employed to re-estimate the changes of BAM. Finally, the detected results are merged with HAM and obtain the final detected binary change maps (BCMs). Experiments are performed on three real-world hyperspectral image datasets, and the results indicate the good universality and adaptability of the proposed scheme, as well as its superiority over other existing state-of-the-art algorithms.

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Harja, Danaswara Prawira, Andrian Rakhmatsyah, and Muhammad Arief Nugroho. "Implementasi untuk Meningkatkan Keamanan Jaringan Menggunakan Deep Packet Inspection pada Software Defined Networks." Indonesian Journal on Computing (Indo-JC) 4, no. 1 (March 22, 2019): 133. http://dx.doi.org/10.21108/indojc.2019.4.1.286.

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AbstractToday, Software Defined Network (SDN) has been globally recognized as a new technology for network architecture. But, there is still lack in security. Many studies use methods such as the Intrusion Prevention System (IPS) and Intrusion Detection System (IDS) to deal with social problems. But there is still a lack of security in terms of network performance. To solve the problem, can be used Deep Packet Inspection method (DPII) which make administrators can directly know what happens to traffic in real time. In this research, DPI will be implemented as security method and tested with Denial of Service (DoS) attack with Direct Attack. The results of testing on SDN networks that have been added DPI can perform packet detection such as IDS and blocking such as IPS with good performance time in overcoming attack.Keywords: SDN, DPI, DoS attack, Direct Attack, performance

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Zhang, Lianming, Yong Lu, Dian Zhang, Haoran Cheng, and Pingping Dong. "DSOQR: Deep Reinforcement Learning for Online QoS Routing in SDN-Based Networks." Security and Communication Networks 2022 (November 29, 2022): 1–11. http://dx.doi.org/10.1155/2022/4457645.

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With the rapid development of mobile communication technology, there are an increasing number of new network applications and services, and the existing best-effort routing algorithms cannot meet the quality-of-service (QoS) requirements of these applications and services. QoS-routing optimization solutions based on a software-defined network (SDN) are often targeted at specific network scenarios and difficult to adapt to changing business requirements. The current routing algorithms based on machine learning (ML) methods can generally only handle discrete, low-dimensional action spaces and use offline network data for training, which is not effective for dynamic network environments. In this study, we propose DSOQR, which is an online QoS-routing framework based on deep reinforcement learning (DRL) and SDN. DSOQR collects network status information in real time through the software-defined paradigm and carries out on-policy learning. Under this framework, we further propose SA3CR, which is a QoS-routing algorithm based on SDN and asynchronous advantage actor-critic (A3C). The SA3CR algorithm can dynamically switch routing paths that meet the conditions according to the current network status and the needs of different service types to ensure the QoS of target traffic. Experimental results show that DSOQR is effective and that the SA3CR algorithm has better performance in terms of delay, throughput, and packet loss rate.

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Chaganti, Rajasekhar, Wael Suliman, Vinayakumar Ravi, and Amit Dua. "Deep Learning Approach for SDN-Enabled Intrusion Detection System in IoT Networks." Information 14, no. 1 (January 9, 2023): 41. http://dx.doi.org/10.3390/info14010041.

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Owing to the prevalence of the Internet of things (IoT) devices connected to the Internet, the number of IoT-based attacks has been growing yearly. The existing solutions may not effectively mitigate IoT attacks. In particular, the advanced network-based attack detection solutions using traditional Intrusion detection systems are challenging when the network environment supports traditional as well as IoT protocols and uses a centralized network architecture such as a software defined network (SDN). In this paper, we propose a long short-term memory (LSTM) based approach to detect network attacks using SDN supported intrusion detection system in IoT networks. We present an extensive performance evaluation of the machine learning (ML) and deep learning (DL) model in two SDNIoT-focused datasets. We also propose an LSTM-based architecture for the effective multiclass classification of network attacks in IoT networks. Our evaluation of the proposed model shows that our model effectively identifies the attacks and classifies the attack types with an accuracy of 0.971. In addition, various visualization methods are shown to understand the dataset’s characteristics and visualize the embedding features.

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Lei, Kai, Yuzhi Liang, and Wei Li. "Congestion Control in SDN-Based Networks via Multi-Task Deep Reinforcement Learning." IEEE Network 34, no. 4 (July 2020): 28–34. http://dx.doi.org/10.1109/mnet.011.1900408.

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Дисертації з теми "Stretched Deep Networks (SDN)"

Nasim, Kamraan. "AETOS: An Architecture for Offloading Core LTE Traffic Using Software Defined Networking Concepts." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/35085.

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It goes without saying that cellular users of today have an insatiable appetite for bandwidth and data. Data-intensive applications, such as video on demand, online gaming and video conferencing, have gained prominence. This, coupled with recent innovations in the mobile network such as LTE/4G, poses a unique challenge to network operators in how to extract the most value from their deployments all the while reducing their Total Cost of Operations(TCO). To this end, a number of enhancements have been proposed to the ”conventional” LTE mobile network. Most of these recognize the monolithic and non-elastic nature of the mobile backend and propose complimenting core functionality with concepts borrowed from Software Defined Networking (SDN). In this thesis we shall attempt to explore some existing options within the LTE standard to mitigate large traffic churns. We will then review some SDN-enabled alternatives, and attempt to derive a proof based critique on their merits and drawbacks.

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Rasool, Raihan Ur. "CyberPulse: A Security Framework for Software-Defined Networks." Thesis, 2020. https://vuir.vu.edu.au/42172/.

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Software-Defined Networking (SDN) technology provides a new perspective in traditional network management by separating infrastructure plane from the control plane which facilitates a higher level of programmability and management. While centralized control provides lucrative benefits, the control channel becomes a bottleneck and home to numerous attacks. We conduct a detailed study and find that crossfire Link Flooding Attacks (LFA) are one of the most lethal attacks for SDN due to the utilization of low-rate traffic and persistent attacking nature. LFAs can be launched by the malicious adversaries to congest the control plane with low-rate traffic which can obstruct the flow rule installation and can ultimately bring down the whole network. Similarly, the adversary can employ bots to generate low-rate traffic to congest the control channel, and ultimately bring down the control plane and data plane connection causing service disruption. We present a systematic and comparative study on the vulnerabilities of LFAs on all the SDN planes, elaborate in detail the LFA types, techniques, and their behavior in all the variant of SDN. We then illustrate the importance of a defense mechanism employing a distributed strategy against LFAs and propose a Machine Learning (ML) based framework namely CyberPulse. Its detailed design, components, and their interaction, working principles, implementation, and in-depth evaluation are presented subsequently. This research presents a novel approach to write anomaly patterns and makes a significant contribution by developing a pattern-matching engine as the first line of defense against known attacks at a line-speed. The second important contribution is the effective detection and mitigation of LFAs in SDN through deep learning techniques. We perform twofold experiments to classify and mitigate LFAs. In the initial experimental setup, we utilize Artificial Neural Networks backward propagation technique to effectively classify the incoming traffic. In the second set of experiments, we employ a holistic approach in which CyberPulse demonstrates algorithm agnostic behavior and employs a pre-trained ML repository for precise classification. As an important scientific contribution, CyberPulse framework has been developed ground up using modern software engineering principles and hence provides very limited bandwidth and computational overhead. It has several useful features such as large-scale network-level monitoring, real-time network status information, and support for a wide variety of ML algorithms. An extensive evaluation is performed using Floodlight open-source controller which shows that CyberPulse offers limited bandwidth and computational overhead and proactively detect and defend against LFA in real-time. This thesis contributes to the state-of-the-art by presenting a novel framework for the defense, detection, and mitigation of LFA in SDN by utilizing ML-based classification techniques. Existing solutions in the area mandate complex hardware for detection and defense, but our presented solution offers a unique advantage in the sense that it operates on real-time traffic scenario as well as it utilizes multiple ML classification algorithms for LFA traffic classification without necessitating complex and expensive hardware. In the future, we plan to implement it on a large testbed and extend it by training on multiple datasets for multiple types of attacks.

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Книги з теми "Stretched Deep Networks (SDN)"

Martin, Brett. Difficult Men. Faber and Faber Limited, 2013. http://dx.doi.org/10.5040/9780571343409.

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In the late 1990s and early 2000s, the landscape of television began an unprecedented transformation. While the networks continued to chase the lowest common denominator, a wave of new shows on cable channels dramatically stretched television's narrative inventiveness, emotional resonance, and artistic ambition. Combining deep reportage with cultural analysis and historical context, Brett Martin recounts the rise and inner workings of a genre that represents not only a new golden age for TV, but also a cultural watershed. Difficult Men features extensive interviews with all the major players, including David Chase, David Simon, David Milch, and Alan Ball; in addition to other writers, executives, directors and actors. Martin delivers never-before-heard story after story, revealing how cable television became a truly significant and influential part of our culture.

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Частини книг з теми "Stretched Deep Networks (SDN)"

Tang, Tuan Anh, Des McLernon, Lotfi Mhamdi, Syed Ali Raza Zaidi, and Mounir Ghogho. "Intrusion Detection in SDN-Based Networks: Deep Recurrent Neural Network Approach." In Deep Learning Applications for Cyber Security, 175–95. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13057-2_8.

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Nguyen, Tri Gia, Trung V. Phan, Dinh Thai Hoang, Tu N. Nguyen, and Chakchai So-In. "Efficient SDN-Based Traffic Monitoring in IoT Networks with Double Deep Q-Network." In Computational Data and Social Networks, 26–38. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66046-8_3.

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Rai, Prerna, and Hiren Kumar Deva Sarma. "A Survey on Application of LSTM as a Deep Learning Approach in Traffic Classification for SDN." In Lecture Notes in Networks and Systems, 161–73. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-5090-2_16.

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AlEroud, Ahmed, and George Karabatis. "SDN-GAN: Generative Adversarial Deep NNs for Synthesizing Cyber Attacks on Software Defined Networks." In On the Move to Meaningful Internet Systems: OTM 2019 Workshops, 211–20. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40907-4_23.

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Xing, Ziyang, Hui Qi, Xiaoqiang Di, Jinyao Liu, and Ligang Cong. "Deep Reinforcement Learning Based Congestion Control Mechanism for SDN and NDN in Satellite Networks." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 13–29. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-34497-8_2.

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Hande, Yogita, and Akkalashmi Muddana. "A Survey on Intrusion Detection System for Software Defined Networks (SDN)." In Research Anthology on Artificial Intelligence Applications in Security, 467–89. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7705-9.ch023.

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Do, Thi Thu Hien, Ba Truc Le, The Duy Phan, Thi Huong Lan Do, Do Hoang Hien, and Van-Hau Pham. "Intrusion Detection with Big Data Analysis in SDN-Enabled Networks." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2022. http://dx.doi.org/10.3233/faia220284.

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Although Software-defined networking (SDN) is a promising architecture that simplifies network management and control, it also faces security problems that may affect the whole network. Hence, protecting strategies, such as intrusion detection and prevention system (IDPS), are in need in the SDN context. The potential of machine learning-based solutions can become the motivation of cut-edge deep learning-based intrusion detection system that can leverage the centralized control and view of the controller to secure the underlying infrastructure. However, performing additional IDPS functions in the controller, which needs to process enormous traffic amounts, can overload this component, and slow down the network. This paper introduces an approach of Big Data analysis for intrusion detection system in SDN, named BIDSDN to enhance the classification perfor-mance with a massive amount of network traffic data. Specifically, we leverage Apache Spark to deploy the distributed deep learning – based detector to reduce the processing time on complex algorithms. The experiments conducted on CICIDS2018 dataset with distributed cluster prove the efficacy in tackling the Big Data-related issues in the large-scale network like SDN.

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Duy, Phan The, Nghi Hoang Khoa, Hoang Hiep, Nguyen Ba Tuan, Hien Do Hoang, Do Thi Thu Hien, and Van-Hau Pham. "A Deep Transfer Learning Approach for Flow-Based Intrusion Detection in SDN-Enabled Network." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia210031.

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Revolutionizing operation model of traditional network in programmability, scalability, and orchestration, Software-Defined Networking (SDN) has considered as a novel network management approach for a massive network with heterogeneous devices. However, it is also highly susceptible to security attacks like conventional network. Inspired from the success of different machine learning algorithms in other domains, many intrusion detection systems (IDS) are presented to identify attacks aiming to harm the network. In this paper, leveraging the flow-based nature of SDN, we introduce DeepFlowIDS, a deep learning (DL)-based approach for anomaly detection using the flow analysis method in SDN. Furthermore, instead of using a lot of network properties, we only utilize essential characteristics of traffic flows to analyze with deep neural networks in IDS. This is to reduce the computational and time cost of attack traffic detection. Besides, we also study the practical benefits of applying deep transfer learning from computer vision to intrusion detection. This method can inherit the knowledge of an effective DL model from other contexts to resolve another task in cybersecurity. Our DL-based IDSs are built and trained with the NSL-KDD and CICIDS2018 dataset in both fine-tuning and feature extractor strategy of transfer learning. Then, it is integrated with the SDN controller to analyze traffic flows retrieved from OpenFlow statistics to recognize the anomaly action in the network.

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Тези доповідей конференцій з теми "Stretched Deep Networks (SDN)"

Saied, Wejdene, Nihel Ben Youssef Ben Souayeh, Amina Saadaoui, and Adel Bouhoula. "Deep and Automated SDN Data Plane Analysis." In 2019 International Conference on Software, Telecommunications and Computer Networks (SoftCOM). IEEE, 2019. http://dx.doi.org/10.23919/softcom.2019.8903846.

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Tosounidis, Vasileios, Georgios Pavlidis, and Ilias Sakellariou. "Deep Q-Learning for Load Balancing Traffic in SDN Networks." In SETN 2020: 11th Hellenic Conference on Artificial Intelligence. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3411408.3411423.

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Xu, Jun, Jingyu Wang, Qi Qi, Haifeng Sun, and Bo He. "DEEP NEURAL NETWORKS FOR APPLICATION AWARENESS IN SDN-BASED NETWORK." In 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2018. http://dx.doi.org/10.1109/mlsp.2018.8517088.

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Hande, Yogita, and Akkalakshmi Muddana. "Intrusion Detection System Using Deep Learning for Software Defined Networks (SDN)." In 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT). IEEE, 2019. http://dx.doi.org/10.1109/icssit46314.2019.8987751.

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Lee, Tsung-Han, Lin-Huang Chang, and Chao-Wei Syu. "Deep Learning Enabled Intrusion Detection and Prevention System over SDN Networks." In 2020 IEEE International Conference on Communications Workshops (ICC Workshops). IEEE, 2020. http://dx.doi.org/10.1109/iccworkshops49005.2020.9145085.

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Nugraha, Beny, and Rathan Narasimha Murthy. "Deep Learning-based Slow DDoS Attack Detection in SDN-based Networks." In 2020 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN). IEEE, 2020. http://dx.doi.org/10.1109/nfv-sdn50289.2020.9289894.

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Tang, Tuan A., Lotfi Mhamdi, Des McLernon, Syed Ali Raza Zaidi, and Mounir Ghogho. "Deep Recurrent Neural Network for Intrusion Detection in SDN-based Networks." In 2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft). IEEE, 2018. http://dx.doi.org/10.1109/netsoft.2018.8460090.

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Swain, Pravati, Uttam Kamalia, Raj Bhandarkar, and Tejas Modi. "CoDRL: Intelligent Packet Routing in SDN Using Convolutional Deep Reinforcement Learning." In 2019 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS). IEEE, 2019. http://dx.doi.org/10.1109/ants47819.2019.9118112.

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Tian, Feng, Yang Zhang, Wei Ye, Cheng Jin, Ziyan Wu, and Zhi-Li Zhang. "Accelerating Distributed Deep Learning using Multi-Path RDMA in Data Center Networks." In SOSR '21: The ACM SIGCOMM Symposium on SDN Research. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3482898.3483363.

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Su, Jing, Suku Nair, and Leo Popokh. "Optimal Resource Allocation in SDN/NFV-Enabled Networks via Deep Reinforcement Learning." In 2022 IEEE Ninth International Conference on Communications and Networking (ComNet). IEEE, 2022. http://dx.doi.org/10.1109/comnet55492.2022.9998475.

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