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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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<p><strong>Abstract</strong></p><p>Today, 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.</p><p><strong>Keywords: </strong>SDN, DPI, DoS attack, Direct Attack, performance</p>
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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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Fu, Qiongxiao, Enchang Sun, Kang Meng, Meng Li, and Yanhua Zhang. "Deep Q-Learning for Routing Schemes in SDN-Based Data Center Networks." IEEE Access 8 (2020): 103491–99. http://dx.doi.org/10.1109/access.2020.2995511.
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Pei, Jianing, Peilin Hong, Miao Pan, Jiangqing Liu, and Jingsong Zhou. "Optimal VNF Placement via Deep Reinforcement Learning in SDN/NFV-Enabled Networks." IEEE Journal on Selected Areas in Communications 38, no. 2 (February 2020): 263–78. http://dx.doi.org/10.1109/jsac.2019.2959181.
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Umair, Muhammad Basit, Zeshan Iqbal, Farrukh Zeeshan Khan, Muhammad Attique Khan, and Seifedine Kadry. "A Deep Learning Based Method for Network Application Classification in Software-Defined IoT." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 30, no. 03 (June 2022): 463–77. http://dx.doi.org/10.1142/s0218488522400165.
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Network Application Classification (NAC) is a vital technology for intrusion detection, Quality-of-Service (QoS)-aware traffic engineering, traffic analysis, and network anomalies. Researchers have focused on designing algorithms using deep learning models based on statistical information to address the challenges of traditional payload and port-based traffic classification techniques. Internet of Things (IoT) and Software Defined Network (SDN) are two popular technologies nowadays and aims to connect devices over the internet and intelligently control networks from a centralized space. IoT aims to connect billions of devices; therefore, classification is essential for efficient processing. SDN is a new networking paradigm, which separates data plane measurement from the control plane. The emergence of deep learning algorithms with SDN provides a scalable traffic classification architecture. Due to the inadequate results of payload and port-based approaches, a statistical technique to classify network traffic into different classes using a Convolution Neural Network (CNN) and a Recurrent Neural Network (RNN) is presented in this paper. This paper provides a classification method for software defined IoT networks. The results show that, contrary to other traffic classification methods, the proposed approach offered a better accuracy rate of over 99 %, which is promising.
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Li, Guoyan, Yihui Shang, Yi Liu, and Xiangru Zhou. "A Network Traffic Prediction Model Based on Graph Neural Network in Software-Defined Networking." International Journal of Information Security and Privacy 16, no. 1 (January 1, 2022): 1–17. http://dx.doi.org/10.4018/ijisp.309130.
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The software-defined network (SDN) is a new network architecture system that achieves the separation of the data plane and the control plane, making SDN networks more relevant to research. Real-time accurate network traffic prediction plays a crucial role in SDN networks, and the spatio-temporal correlation and autocorrelation of SDN make traditional methods unable to meet the requirements of the prediction tasks. In this article, a SDN network traffic prediction model DI-GCN (deep information-GCN) is proposed, which firstly fuses graph convolution with gated convolutional units; secondly, the matrix of mutual information relation is defined and constructed to obtain the relational weight representation of traffic data. The proposed model was compared with GCN, GRU, and T-GCN on the real dataset GÉANT, respectively. Experiments show that the DI-GCN model not only ensures the ability to represent the actual data but also reduces the prediction error as well as achieved better prediction results.
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Xu, Chenglin, Cheng Xu, and Bo Li. "Multi-Agent Deep Q-Network Based Dynamic Controller Placement for Node Variable Software-Defined Mobile Edge-Cloud Computing Networks." Mathematics 11, no. 5 (March 4, 2023): 1247. http://dx.doi.org/10.3390/math11051247.
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Software-defined networks (SDN) can use the control plane to manage heterogeneous devices efficiently, improve network resource utilization, and optimize Mobile Edge-Cloud Computing Networks (MECCN) network performance through decisions based on global information. However, network traffic in MECCNs can change over time and affect the performance of the SDN control plane. Moreover, the MECCN network may need to temporarily add network access points when the network load is excessive, and it is difficult for the control plane to form effective management of temporary nodes. This paper investigates the dynamic controller placement problem (CPP) in SDN-enabled Mobile Edge-Cloud Computing Networks (SD-MECCN) to enable the control plane to continuously and efficiently serve the network under changing network load and network access points. We consider the deployment of a two-layer structure with a control plane and construct the CPP based on this control plane. Subsequently, we solve this problem based on multi-agent DQN (MADQN), in which multiple agents cooperate to solve CPP and adjust the number of controllers according to the network load. The experimental results show that the proposed dynamic controller deployment algorithm based on MADQN for node-variable networks in this paper can achieve better performance in terms of delay, load difference, and control reliability than the Louvain-based algorithm, single-agent DQN-based algorithm, and MADQN- (without node-variable networks consideration) based algorithm.
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Lei, Fangyuan, Jun Cai, Qingyun Dai, and Huimin Zhao. "Deep Learning Based Proactive Caching for Effective WSN-Enabled Vision Applications." Complexity 2019 (May 2, 2019): 1–12. http://dx.doi.org/10.1155/2019/5498606.
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Wireless Sensor Networks (WSNs) have a wide range of applications scenarios in computer vision, from pedestrian detection to robotic visual navigation. In response to the growing visual data services in WSNs, we propose a proactive caching strategy based on Stacked Sparse Autoencoder (SSAE) to predict content popularity (PCDS2AW). Firstly, based on Software Defined Network (SDN) and Network Function Virtualization (NFV) technologies, a distributed deep learning network SSAE is constructed in the sink nodes and control nodes of the WSN network. Then, the SSAE network structure parameters and network model parameters are optimized through training. The proactive cache strategy implementation procedure is divided into four steps. (1) The SDN controller is responsible for dynamically collecting user request data package information in the WSNs network. (2) The SSAEs predicts the packet popularity based on the SDN controller obtaining user request data. (3) The SDN controller generates a corresponding proactive cache strategy according to the popularity prediction result. (4) Implement the proactive caching strategy at the WSNs cache node. In the simulation, we compare the influence of spatiotemporal data on the SSAE network structure. Compared with the classic caching strategy Hash + LRU, Betw + LRU, and classic prediction algorithms SVM and BPNN, the proposed PCDS2AW proactive caching strategy can significantly improve WSN performance.
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Song, Inseok, Prohim Tam, Seungwoo Kang, Seyha Ros, and Seokhoon Kim. "DRL-Based Backbone SDN Control Methods in UAV-Assisted Networks for Computational Resource Efficiency." Electronics 12, no. 13 (July 6, 2023): 2984. http://dx.doi.org/10.3390/electronics12132984.
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The limited coverage extension of mobile edge computing (MEC) necessitates exploring cooperation with unmanned aerial vehicles (UAV) to leverage advanced features for future computation-intensive and mission-critical applications. Moreover, the workflow for task offloading in software-defined networking (SDN)-enabled 5G is significant to tackle in UAV-MEC networks. In this paper, deep reinforcement learning (DRL) SDN control methods for improving computing resources are proposed. DRL-based SDN controller, termed DRL-SDNC, allocates computational resources, bandwidth, and storage based on task requirements, upper-bound tolerable delays, and network conditions, using the UAV system architecture for task exchange between MECs. DRL-SDNC configures rule installation based on state observations and agent evaluation indicators, such as network congestion, user equipment computational capabilities, and energy efficiency. This paper also proposes the training deep network architecture for the DRL-SDNC, enabling interactive and autonomous policy enforcement. The agent learns from the UAV-MEC environment through experience gathering and updates its parameters using optimization methods. DRL-SDNC collaboratively adjusts hyperparameters and network architecture to enhance learning efficiency. Compared with baseline schemes, simulation results demonstrate the effectiveness of the proposed approach in optimizing resource efficiency and achieving satisfied quality of service for efficient utilization of computing and communication resources in UAV-assisted networking environments.
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Shazly, Khadija, Dina A. Salem, Nacereddine Hammami, and Ahmed I. B. ElSeddawy. "A Review on Distributed Denial of Service Detection in Software Defined Network." International Journal of Wireless and Ad Hoc Communication 5, no. 2 (2022): 08–18. http://dx.doi.org/10.54216/ijwac.050201.
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Network security has become considerably essential because of the expansion of the internet of things (IoT) devices. One of the greatest hazards of today's networks is distributed denial of service (DDoS) attacks, which could destroy critical network services. Recently numerous IoT devices are unsuspectingly attacked by DDoS. To securely manage IoT equipment, researchers have introduced software-defined networks (SDN). This paper aims to analyze and discuss machine learning-based systems for SDN security networks from DDoS attacks. The results have indicated that the algorithms for machine learning can be used to detect DDoS attacks in SDN efficiently. From machine learning approaches, it can be explored that the best way to detect DDoS attacks is based on utilizing deep learning procedures. Moreover, analyze the methods that combine it with other machine learning techniques. The most benefits that can be achieved from using deep learning methods are the ability to do both feature extraction along with data classification; the ability to extract specific information from partial data. Nevertheless, it is appropriate to recognize the low-rate attack, and it can get more computation resources than other machine learning where it can use a graphics processing unit (GPU) rather than a central processing unit (CPU) for carrying out the matrix operations, making the processes computationally effective and fast.
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Mousa, Amthal K., and Mohammed Najm Abdullah. "An Improved Deep Learning Model for DDoS Detection Based on Hybrid Stacked Autoencoder and Checkpoint Network." Future Internet 15, no. 8 (August 19, 2023): 278. http://dx.doi.org/10.3390/fi15080278.
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The software defined network (SDN) collects network traffic data and proactively manages networks. SDN’s programmability makes it excellent for developing distributed applications, cybersecurity, and decentralized network control in multitenant data centers. This exceptional architecture is vulnerable to security concerns, such as distributed denial of service (DDoS) attacks. DDoS attacks can be very serious due to the fact that they prevent authentic users from accessing, temporarily or indefinitely, resources they would normally expect to have. Moreover, there are continuous efforts from attackers to produce new techniques to avoid detection. Furthermore, many existing DDoS detection methods now in use have a high potential for producing false positives. This motivates us to provide an overview of the research studies that have already been conducted in this area and point out the strengths and weaknesses of each of those approaches. Hence, adopting an optimal detection method is necessary to overcome these issues. Thus, it is crucial to accurately detect abnormal flows to maintain the availability and security of the network. In this work, we propose hybrid deep learning algorithms, which are the long short-term memory network (LSTM) and convolutional neural network (CNN) with a stack autoencoder for DDoS attack detection and checkpoint network, which is a fault tolerance strategy for long-running processes. The proposed approach is trained and tested with the aid of two DDoS attack datasets in the SDN environment: the DDoS attack SDN dataset and Botnet dataset. The results show that the proposed model achieves a very high accuracy, reaching 99.99% in training, 99.92% in validation, and 100% in precision, recall, and F1 score with the DDoS attack SDN dataset. Also, it achieves 100% in all metrics with the Botnet dataset. Experimental results reveal that our proposed model has a high feature extraction ability and high performance in detecting attacks. All performance metrics indicate that the proposed approach is appropriate for a real-world flow detection environment.
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Negera, Worku Gachena, Friedhelm Schwenker, Taye Girma Debelee, Henock Mulugeta Melaku, and Yehualashet Megeresa Ayano. "Review of Botnet Attack Detection in SDN-Enabled IoT Using Machine Learning." Sensors 22, no. 24 (December 14, 2022): 9837. http://dx.doi.org/10.3390/s22249837.
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The orchestration of software-defined networks (SDN) and the internet of things (IoT) has revolutionized the computing fields. These include the broad spectrum of connectivity to sensors and electronic appliances beyond standard computing devices. However, these networks are still vulnerable to botnet attacks such as distributed denial of service, network probing, backdoors, information stealing, and phishing attacks. These attacks can disrupt and sometimes cause irreversible damage to several sectors of the economy. As a result, several machine learning-based solutions have been proposed to improve the real-time detection of botnet attacks in SDN-enabled IoT networks. The aim of this review is to investigate research studies that applied machine learning techniques for deterring botnet attacks in SDN-enabled IoT networks. Initially the first major botnet attacks in SDN-IoT networks have been thoroughly discussed. Secondly a commonly used machine learning techniques for detecting and mitigating botnet attacks in SDN-IoT networks are discussed. Finally, the performance of these machine learning techniques in detecting and mitigating botnet attacks is presented in terms of commonly used machine learning models’ performance metrics. Both classical machine learning (ML) and deep learning (DL) techniques have comparable performance in botnet attack detection. However, the classical ML techniques require extensive feature engineering to achieve optimal features for efficient botnet attack detection. Besides, they fall short of detecting unforeseen botnet attacks. Furthermore, timely detection, real-time monitoring, and adaptability to new types of attacks are still challenging tasks in classical ML techniques. These are mainly because classical machine learning techniques use signatures of the already known malware both in training and after deployment.
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Ling, Carlos, Konrad Tollmar, and Linus Gisslén. "Using Deep Convolutional Neural Networks to Detect Rendered Glitches in Video Games." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 16, no. 1 (October 1, 2020): 66–73. http://dx.doi.org/10.1609/aiide.v16i1.7409.
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In this paper, we present a method using Deep Convolutional Neural Networks (DCNNs) to detect common glitches in video games. The problem setting consists of an image (800x800 RGB) as input to be classified into one of five defined classes, normal image, or one of four different kinds of glitches (stretched, low resolution, missing and placeholder textures). Using a supervised approach, we train a ShuffleNetV2 using generated data. This work focuses on detecting texture graphical anomalies achieving arguably good performance with an accuracy of 86.8%, detecting 88% of the glitches with a false positive rate of 8.7%, and with the models being able to generalize and detect glitches even in unseen objects. We apply a confidence measure as well to tackle the issue with false positives as well as an effective way of aggregating images to achieve better detection in production. The main use of this work is the partial automatization of graphical testing in the final stages of video game development.
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Ye, Jin, Xiangyang Cheng, Jian Zhu, Luting Feng, and Ling Song. "A DDoS Attack Detection Method Based on SVM in Software Defined Network." Security and Communication Networks 2018 (2018): 1–8. http://dx.doi.org/10.1155/2018/9804061.
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The detection of DDoS attacks is an important topic in the field of network security. The occurrence of software defined network (SDN) (Zhang et al., 2018) brings up some novel methods to this topic in which some deep learning algorithm is adopted to model the attack behavior based on collecting from the SDN controller. However, the existing methods such as neural network algorithm are not practical enough to be applied. In this paper, the SDN environment by mininet and floodlight (Ning et al., 2014) simulation platform is constructed, 6-tuple characteristic values of the switch flow table is extracted, and then DDoS attack model is built by combining the SVM classification algorithms. The experiments show that average accuracy rate of our method is 95.24% with a small amount of flow collecting. Our work is of good value for the detection of DDoS attack in SDN.
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Chen, Junyan, Cenhuishan Liao, Yong Wang, Lei Jin, Xiaoye Lu, Xiaolan Xie, and Rui Yao. "AQMDRL: Automatic Quality of Service Architecture Based on Multistep Deep Reinforcement Learning in Software-Defined Networking." Sensors 23, no. 1 (December 30, 2022): 429. http://dx.doi.org/10.3390/s23010429.
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Software-defined networking (SDN) has become one of the critical technologies for data center networks, as it can improve network performance from a global perspective using artificial intelligence algorithms. Due to the strong decision-making and generalization ability, deep reinforcement learning (DRL) has been used in SDN intelligent routing and scheduling mechanisms. However, traditional deep reinforcement learning algorithms present the problems of slow convergence rate and instability, resulting in poor network quality of service (QoS) for an extended period before convergence. Aiming at the above problems, we propose an automatic QoS architecture based on multistep DRL (AQMDRL) to optimize the QoS performance of SDN. AQMDRL uses a multistep approach to solve the overestimation and underestimation problems of the deep deterministic policy gradient (DDPG) algorithm. The multistep approach uses the maximum value of the n-step action currently estimated by the neural network instead of the one-step Q-value function, as it reduces the possibility of positive error generated by the Q-value function and can effectively improve convergence stability. In addition, we adapt a prioritized experience sampling based on SumTree binary trees to improve the convergence rate of the multistep DDPG algorithm. Our experiments show that the AQMDRL we proposed significantly improves the convergence performance and effectively reduces the network transmission delay of SDN over existing DRL algorithms.
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Mansoor, Amran, Mohammed Anbar, Abdullah Ahmed Bahashwan, Basim Ahmad Alabsi, and Shaza Dawood Ahmed Rihan. "Deep Learning-Based Approach for Detecting DDoS Attack on Software-Defined Networking Controller." Systems 11, no. 6 (June 9, 2023): 296. http://dx.doi.org/10.3390/systems11060296.
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The rapid growth of cloud computing has led to the development of the Software-Defined Network (SDN), which is a network strategy that offers dynamic management and improved performance. However, security threats are a growing concern, particularly with the SDN controller becoming an attractive target for malicious actors and potential Distributed Denial of Service (DDoS) attacks. Many researchers have proposed different approaches to detecting DDoS attacks. However, those approaches suffer from high false positives, leading to low accuracy, and the main reason behind this is the use of non-qualified features and non-realistic datasets. Therefore, the deep learning (DL) algorithmic technique can be utilized to detect DDoS attacks on SDN controllers. Moreover, the proposed approach involves three stages, (1) data preprocessing, (2) cross-feature selection, which aims to identify important features for DDoS detection, and (3) detection using the Recurrent Neural Networks (RNNs) model. A benchmark dataset is employed to evaluate the proposed approach via standard evaluation metrics, including false positive rate and detection accuracy. The findings indicate that the recommended approach effectively detects DDoS attacks with average detection accuracy, average precision, average FPR, and average F1-measure of 94.186 %, 92.146%, 8.114%, and 94.276%, respectively.
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Chuang, Hsiu-Min, Fanpyn Liu, and Chung-Hsien Tsai. "Early Detection of Abnormal Attacks in Software-Defined Networking Using Machine Learning Approaches." Symmetry 14, no. 6 (June 8, 2022): 1178. http://dx.doi.org/10.3390/sym14061178.
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Recent developments have made software-defined networking (SDN) a popular technology for solving the inherent problems of conventional distributed networks. The key benefit of SDN is the decoupling between the control plane and the data plane, which makes the network more flexible and easier to manage. SDN is a new generation network architecture; however, its configuration settings are centralized, making it vulnerable to hackers. Our study investigated the feasibility of applying artificial intelligence technology to detect abnormal attacks in an SDN environment based on the current unit network architecture; therefore, the concept of symmetry includes the sustainability of SDN applications and robust performance of machine learning (ML) models in the case of various malicious attacks. In this study, we focus on the early detection of abnormal attacks in an SDN environment. On detection of malicious traffic in SDN topology, the AI module in the topology is applied to detect and act against the attack source through machine learning algorithms, making the network architecture more flexible. Under multiple abnormal attacks, we propose a hierarchical multi-class (HMC) architecture to effectively address the imbalanced dataset problem and improve the performance of minority classes. The experimental results show that the decision tree, random forest, bagging, AdaBoost, and deep learning models exhibit the best performance for distributed denial-of-service (DDoS) attacks. In addition, for the imbalanced dataset problem of multiclass classification, our proposed HMC architecture performs better than previous single classifiers. We also simulated the SDN topology and scenario verification. In summary, we concatenated the AI module to enhance the security and effectiveness of SDN networks in a practical manner.
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Et. al., Shilpa P. Khedkar,. "A Deep Learning method for effective channel allotment for SDN based IOT." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 10, 2021): 1721–28. http://dx.doi.org/10.17762/turcomat.v12i2.1508.
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Due to advances in the field of internet of things (IoT), the transmission speed become very important and need to be discussed. Doing proper assignment of appropriate channels to the generated traffic in SDN based IoT can affect transmission speed enormously. Software Defined Networking has been evolved as a supporting technology to improve the performance of IoT networks and to increase transmission quality. Different machine learning algorithm can be used for prediction of network traffic and allocation of the channel is done for better assignment. Hence, in this paper CNNs based network traffic prediction and allocation of channel technique is proposed. This technique significantly improves the network performance.
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MAJDOUB, MANEL, ALI EL KAMEL, and HABIB YOUSSEF. "DQR: An Efficient Deep Q-Based Routing Approach in Multi-Controller Software Defined WAN (SD-WAN)." Journal of Interconnection Networks 20, no. 04 (December 2020): 2150002. http://dx.doi.org/10.1142/s021926592150002x.
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Software Defined Networking (SDN) is a promising paradigm in the field of network technology. This paradigm suggests the separation between the control plane and the data plane which brings flexibility, efficiency and programmability to network resources. SDN deployment in large scale networks raises many issues which can be overcame using a collaborative multi-controller approaches. Such approaches can resolve problems of routing optimization and network scalability. In large scale networks, such as SD-WAN, routing optimization consists of achieving a trade-off between per-flow QoS, the load balancing in each domain as well as the resource utilization in inter-domain links. Multi-Agent Reinforcement Learning paradigm(MARL) is one of the most popular solutions that can be used to optimize routing strategies in SD-WAN. This paper proposes an efficient approach based on MARL which is able to ensure a load balancing among each network as well as optimized resource utilization of inter-domain links. This approach profits from our previous work, denoted SPFLR, and tries to balance the load of the whole network using Deep Q-Networks (DQN) algorithms. Simulation results show that the proposed solution performs better than parallel solutions such as BGP-based routing and random routing.
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Kou, Liang, Shanshuo Ding, Ting Wu, Wei Dong, and Yuyu Yin. "An Intrusion Detection Model for Drone Communication Network in SDN Environment." Drones 6, no. 11 (November 4, 2022): 342. http://dx.doi.org/10.3390/drones6110342.
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Drone communication is currently a hot topic of research, and the use of drones can easily set up communication networks in areas with complex terrain or areas subject to disasters and has broad application prospects. One of the many challenges currently facing drone communication is the communication security issue. Drone communication networks generally use software defined network (SDN) architectures, and SDN controllers can provide reliable data forwarding control for drone communication networks, but they are also highly susceptible to attacks and pose serious security threats to drone networks. In order to solve the security problem, this paper proposes an intrusion detection model that can reach the convergence state quickly. The model consists of a deep auto-encoder (DAE), a convolutional neural network (CNN), and an attention mechanism. DAE is used to reduce the original data dimensionality and improve the training efficiency, CNN is used to extract the data features, the attention mechanism is used to enhance the important features of the data, and finally the traffic is detected and classified. We conduct tests using the InSDN dataset, which is collected from an SDN environment and is able to verify the effectiveness of the model on SDN traffic. The experiments utilize the Tensorflow framework to build a deep learning model structure, which is run on the Jupyter Notebook platform in the Anaconda environment. Compared with the CNN model, the LSTM model, and the CNN+LSTM hybrid model, the accuracy of this model in binary classification experiments is 99.7%, which is about 0.6% higher than other comparison models. The accuracy of the model in the multiclassification experiment is 95.5%, which is about 3% higher than other comparison models. Additionally, it only needs 20 to 30 iterations to converge, which is only one-third of other models. The experiment proves that the model has fast convergence speed and high precision and is an effective detection method.
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Saleh, Sherine Nagy, and Cherine Fathy. "A Novel Deep-Learning Model for Remote Driver Monitoring in SDN-Based Internet of Autonomous Vehicles Using 5G Technologies." Applied Sciences 13, no. 2 (January 8, 2023): 875. http://dx.doi.org/10.3390/app13020875.
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The rapid advancement in the Internet of Things (IoT) and its integration with Artificial Intelligence (AI) techniques are expected to play a crucial role in future Intelligent Transportation Systems (ITS). Additionally, the continuous progress in the industry of autonomous vehicles will accelerate and increase their short adoption in smart cities to allow safe, sustainable and accessible trips for passengers in different public and private means of transportation. In this article, we investigate the adoption of 5G different technologies, mainly, the Software-Defined Networks (SDN) to support the communication requirements of delegation of control of level-2 autonomous vehicles to the Remote-Control Center (RCC) in terms of ultra-low delay and reliability. This delegation occurs upon the detection of a drowsy driver using our proposed deep-learning-based technique deployed at the edge to reduce the level of accidents and road congestion. The deep learning-based model was evaluated and produced higher accuracy, precision and recall when compared to other methods. The role of SDN is to implement network slicing to achieve the Quality of Service (QoS) level required in this emergency case. Decreasing the end-to-end delay required to provide feedback control signals back to the autonomous vehicle is the aim of deploying QoS support available in an SDN-based network. Feedback control signals are sent to remotely activate the stopping system or to switch the vehicle to direct teleoperation mode. The mininet-WiFi emulator is deployed to evaluate the performance of the proposed adaptive SDN framework, which is tailored to emulate radio access networks. Our simulation experiments conducted on realistic vehicular scenarios revealed significant improvement in terms of throughput and average Round-Trip Time (RTT).
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Ullah, Ihtisham, Basit Raza, Sikandar Ali, Irshad Ahmed Abbasi, Samad Baseer, and Azeem Irshad. "Software Defined Network Enabled Fog-to-Things Hybrid Deep Learning Driven Cyber Threat Detection System." Security and Communication Networks 2021 (December 3, 2021): 1–15. http://dx.doi.org/10.1155/2021/6136670.
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Software Defined Network (SDN) is a next-generation networking architecture and its power lies in centralized control intelligence. The control plane of SDN can be extended to many underlying networks such as fog to Internet of Things (IoT). The fog-to-IoT is currently a promising architecture to manage a real-time large amount of data. However, most of the fog-to-IoT devices are resource-constrained and devices are widespread that can be potentially targeted with cyber-attacks. The evolving cyber-attacks are still an arresting challenge in the fog-to-IoT environment such as Denial of Service (DoS), Distributed Denial of Service (DDoS), Infiltration, malware, and botnets attacks. They can target varied fog-to-IoT agents and the whole network of organizations. The authors propose a deep learning (DL) driven SDN-enabled architecture for sophisticated cyber-attacks detection in fog-to-IoT environment to identify new attacks targeting IoT devices as well as other threats. The extensive simulations have been carried out using various DL algorithms and current state-of-the-art Coburg Intrusion Detection Data Set (CIDDS-001) flow-based dataset. For better analysis five DL models are compared including constructed hybrid DL models to distinguish the DL model with the best performance. The results show that proposed Long Short-Term Memory (LSTM) hybrid model outperforms other DL models in terms of detection accuracy and response time. To show unbiased results 10-fold cross-validation is performed. The proposed framework is so effective that it can detect several types of cyber-attacks with 99.92% accuracy rate in multiclass classification.
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Khattab M. Ali Alheeti, Abdulkareem Alzahrani, Maha Alamri, Aythem Khairi Kareem, and Duaa Al_Dosary. "A Comparative Study for SDN Security Based on Machine Learning." International Journal of Interactive Mobile Technologies (iJIM) 17, no. 11 (June 7, 2023): 131–40. http://dx.doi.org/10.3991/ijim.v17i11.39065.
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In the past decade, traditional networks have been utilized to transfer data between more than one node. The primary problem related to formal networks is their stable essence, which makes them incapable of meeting the requirements of nodes recently inserted into the network. Thus, formal networks are substituted by a Software Defined Network (SDN). The latter can be utilized to construct a structure for intensive data applications like big data. In this paper, a comparative investigation of Deep Neural Network (DNN) and Machine Learning (ML) techniques that uses various feature selection techniques is undertaken. The ML techniques employed in this approach are decision tree (DT), Naïve Bayes (NB), Support Vector Machine (SVM). The proposed approach is tested experimentally and evaluated using an available NSL–KDD dataset. This dataset includes 41 features and 148,517 samples. To evaluate the techniques, several estimation measurements are calculated. The results prove that DT is the most accurate and effective approach. Furthermore, the evaluation measurements indicate the efficacy of the presented approach compared to earlier studies.
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Hou, Jiacheng, Tianhao Tao, Haoye Lu, and Amiya Nayak. "Intelligent Caching with Graph Neural Network-Based Deep Reinforcement Learning on SDN-Based ICN." Future Internet 15, no. 8 (July 26, 2023): 251. http://dx.doi.org/10.3390/fi15080251.
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Information-centric networking (ICN) has gained significant attention due to its in-network caching and named-based routing capabilities. Caching plays a crucial role in managing the increasing network traffic and improving the content delivery efficiency. However, caching faces challenges as routers have limited cache space while the network hosts tens of thousands of items. This paper focuses on enhancing the cache performance by maximizing the cache hit ratio in the context of software-defined networking–ICN (SDN-ICN). We propose a statistical model that generates users’ content preferences, incorporating key elements observed in real-world scenarios. Furthermore, we introduce a graph neural network–double deep Q-network (GNN-DDQN) agent to make caching decisions for each node based on the user request history. Simulation results demonstrate that our caching strategy achieves a cache hit ratio 34.42% higher than the state-of-the-art policy. We also establish the robustness of our approach, consistently outperforming various benchmark strategies.
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Saqib, Muhammad, Farrukh Zeeshan Khan, Muneer Ahmed, and Raja Majid Mehmood. "A critical review on security approaches to software-defined wireless sensor networking." International Journal of Distributed Sensor Networks 15, no. 12 (December 2019): 155014771988990. http://dx.doi.org/10.1177/1550147719889906.
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Wireless sensor networks (WSNs) are very prone to ongoing security threats due to its resource constraints and unprotected transmission medium. WSN contains hundreds and thousands of resource-constrained and self-organized sensor nodes. These sensor nodes are usually organized in a distributed manner; thus, it permits the creation of an ad hoc network without predefined infrastructure or centralized management. As WSNs are going to get control of real-time applications, where a malicious activity can cause serious damage, the inherent challenge is to fortify the security enforcement in these networks. As a solution, software-defined network (SDN) has come out and has been merged with WSN to form what is known as software-defined wireless sensor network (SDWSN). SDWSN has come into existence, and it legitimizes network operators with more flexibility and control over the network. SDWSN has more tightened the security enforcement based on the global view and centralized control of the network topology. Moreover, machine learning (ML)–based and deep learning (DL)–based network intrusion detection systems (NIDS) have been introduced to the SDN environment to protect the networks against anomaly threats. In this review article, we illustrated the SDN–based security approaches to WSN followed by its architectures, advantages, and possible security threats. Finally, ML/DL–based NIDS integrated with the SDN controller is proposed as a complete solution for the WSN environment to confront the ongoing anomaly threats and to sufficiently protect the network against both known and unknown attacks.
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Meng, Xiangli, Lingda Wu, and Shaobo Yu. "Research on Resource Allocation Method of Space Information Networks Based on Deep Reinforcement Learning." Remote Sensing 11, no. 4 (February 21, 2019): 448. http://dx.doi.org/10.3390/rs11040448.
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The space information networks (SIN) have a series of characteristics, such as strong heterogeneity, multiple types of resources, and difficulty in management. Aiming at the problem of resource allocation in SIN, this paper firstly establishes a hierarchical and domain-controlled SIN architecture based on software-defined networking (SDN). On this basis, the transmission, caching, and computing resources of the whole network are managed uniformly. The Asynchronous Advantage Actor-Critic (A3C) algorithm in deep reinforcement learning is introduced to model the process of resource allocation. The simulation results show that the proposed scheme can effectively improve the expected benefits of unit resources and improve the resource utilization efficiency of the SIN.
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Ravi, Vinayakumar, Rajasekhar Chaganti, and Mamoun Alazab. "Deep Learning Feature Fusion Approach for an Intrusion Detection System in SDN-Based IoT Networks." IEEE Internet of Things Magazine 5, no. 2 (June 2022): 24–29. http://dx.doi.org/10.1109/iotm.003.2200001.
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Ding, Pengpeng, Jinguo Li, Liangliang Wang, Mi Wen, and Yuyao Guan. "HYBRID-CNN: An Efficient Scheme for Abnormal Flow Detection in the SDN-Based Smart Grid." Security and Communication Networks 2020 (August 3, 2020): 1–20. http://dx.doi.org/10.1155/2020/8850550.
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Software-Defined Network (SDN) can improve the performance of the power communication network and better meet the control demand of the Smart Grid for its centralized management. Unfortunately, the SDN controller is vulnerable to many potential network attacks. The accurate detection of abnormal flow is especially important for the security and reliability of the Smart Grid. Prior works were designed based on traditional machine learning methods, such as Support Vector Machine and Naive Bayes. They are simple and shallow feature learning, with low accuracy for large and high-dimensional network flow. Recently, there have been several related works designed based on Long Short-Term Memory (LSTM), and they show excellent ability on network flow analysis. However, these methods cannot get the deep features from network flow, resulting in low accuracy. To address the above problems, we propose a Hybrid Convolutional Neural Network (HYBRID-CNN) method. Specifically, the HYBRID-CNN utilizes a Deep Neural Network (DNN) to effectively memorize global features by one-dimensional (1D) data and utilizes a CNN to generalize local features by two-dimensional (2D) data. Finally, the proposed method is evaluated by experiments on the datasets of UNSW_NB15 and KDDCup 99. The experimental results show that the HYBRID-CNN significantly outperforms existing methods in terms of accuracy and False Positive Rate (FPR), which successfully demonstrates that it can effectively detect abnormal flow in the SDN-based Smart Grid.
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Chen, Yi-Ren, Amir Rezapour, Wen-Guey Tzeng, and Shi-Chun Tsai. "RL-Routing: An SDN Routing Algorithm Based on Deep Reinforcement Learning." IEEE Transactions on Network Science and Engineering 7, no. 4 (October 1, 2020): 3185–99. http://dx.doi.org/10.1109/tnse.2020.3017751.
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Żotkiewicz, Mateusz, Wiktor Szałyga, Jaroslaw Domaszewicz, Andrzej Bąk, Zbigniew Kopertowski, and Stanisław Kozdrowski. "Artificial Intelligence Control Logic in Next-Generation Programmable Networks." Applied Sciences 11, no. 19 (October 2, 2021): 9163. http://dx.doi.org/10.3390/app11199163.
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The new generation of programmable networks allow mechanisms to be deployed for the efficient control of dynamic bandwidth allocation and ensure Quality of Service (QoS) in terms of Key Performance Indicators (KPIs) for delay or loss sensitive Internet of Things (IoT) services. To achieve flexible, dynamic and automated network resource management in Software-Defined Networking (SDN), Artificial Intelligence (AI) algorithms can provide an effective solution. In the paper, we propose the solution for network resources allocation, where the AI algorithm is responsible for controlling intent-based routing in SDN. The paper focuses on the problem of optimal switching of intents between two designated paths using the Deep-Q-Learning approach based on an artificial neural network. The proposed algorithm is the main novelty of this paper. The Developed Networked Application Emulation System (NAPES) allows the AI solution to be tested with different patterns to evaluate the performance of the proposed solution. The AI algorithm was trained to maximize the total throughput in the network and effective network utilization. The results presented confirm the validity of applied AI approach to the problem of improving network performance in next-generation networks and the usefulness of the NAPES traffic generator for efficient economical and technical deployment in IoT networking systems evaluation.
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Sun, Penghao, Zehua Guo, Julong Lan, Junfei Li, Yuxiang Hu, and Thar Baker. "ScaleDRL: A Scalable Deep Reinforcement Learning Approach for Traffic Engineering in SDN with Pinning Control." Computer Networks 190 (May 2021): 107891. http://dx.doi.org/10.1016/j.comnet.2021.107891.
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Chen, Dr Joy Iong Zong, and Dr Smys S. "Social Multimedia Security and Suspicious Activity Detection in SDN using Hybrid Deep Learning Technique." June 2020 2, no. 2 (May 27, 2020): 108–15. http://dx.doi.org/10.36548/jitdw.2020.2.004.
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Social multimedia traffic is growing exponentially with the increased usage and continuous development of services and applications based on multimedia. Quality of Service (QoS), Quality of Information (QoI), scalability, reliability and such factors that are essential for social multimedia networks are realized by secure data transmission. For delivering actionable and timely insights in order to meet the growing demands of the user, multimedia analytics is performed by means of a trust-based paradigm. Efficient management and control of the network is facilitated by limiting certain capabilities such as energy-aware networking and runtime security in Software Defined Networks. In social multimedia context, suspicious flow detection is performed by a hybrid deep learning based anomaly detection scheme in order to enhance the SDN reliability. The entire process is divided into two modules namely – Abnormal activities detection using support vector machine based on Gradient descent and improved restricted Boltzmann machine which facilitates the anomaly detection module, and satisfying the strict requirements of QoS like low latency and high bandwidth in SDN using end-to-end data delivery module. In social multimedia, data delivery and anomaly detection services are essential in order to improve the efficiency and effectiveness of the system. For this purpose, we use benchmark datasets as well as real time evaluation to experimentally evaluate the proposed scheme. Detection of malicious events like confidential data collection, profile cloning and identity theft are performed to analyze the performance of the system using CMU-based insider threat dataset for large scale analysis.
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Dey, Samrat Kumar, and Md Mahbubur Rahman. "Effects of Machine Learning Approach in Flow-Based Anomaly Detection on Software-Defined Networking." Symmetry 12, no. 1 (December 18, 2019): 7. http://dx.doi.org/10.3390/sym12010007.
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Recent advancements in software-defined networking (SDN) make it possible to overcome the management challenges of traditional networks by logically centralizing the control plane and decoupling it from the forwarding plane. Through a symmetric and centralized controller, SDN can prevent security breaches, but it can also bring in new threats and vulnerabilities. The central controller can be a single point of failure. Hence, flow-based anomaly detection system in OpenFlow Controller can secure SDN to a great extent. In this research, we investigated two different approaches of flow-based intrusion detection system in OpenFlow Controller. The first of which is based on machine-learning algorithm where NSL-KDD dataset with feature selection ensures the accuracy of 82% with random forest classifier using the gain ratio feature selection evaluator. In the later phase, the second approach is combined with a deep neural network (DNN)-based intrusion detection system based on gated recurrent unit-long short-term memory (GRU-LSTM) where we used a suitable ANOVA F-Test and recursive feature elimination selection method to boost classifier output and achieve an accuracy of 88%. Substantial experiments with comparative analysis clearly show that, deep learning would be a better choice for intrusion detection in OpenFlow Controller.
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Guo, Xuancheng, Hui Lin, Zhiyang Li, and Min Peng. "Deep-Reinforcement-Learning-Based QoS-Aware Secure Routing for SDN-IoT." IEEE Internet of Things Journal 7, no. 7 (July 2020): 6242–51. http://dx.doi.org/10.1109/jiot.2019.2960033.
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Rizal, Rizal, Martanto Martanto, and Yudhistira Arie Wijaya. "ANALISA DATASET SOFTWARE DEFINED NETWORK INTRUSION MENGGUNAKAN ALGORITMA DEEP LEARNING H2O." JATI (Jurnal Mahasiswa Teknik Informatika) 6, no. 2 (October 31, 2022): 747–57. http://dx.doi.org/10.36040/jati.v6i2.5724.
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Software-defined networking Intrusion (SDNI) baru-baru ini menjadi salah satu solusi paling menjanjikan untuk Internet masa depan. Dengan sentralisasi logis dari pengontrol dan tampilan jaringan global, SDN Intrusion menawarkan peluang untuk meningkatkan keamanan jaringan. Pada penelitian sebelumnya oleh Omar Jamal Ibrahim, dan Wesam S. Bhaya menjelaskan tentang dataset SDN intrusion bahwa dengan menggunakan algoritma Support Vector Machine (SVM) diperoleh dengan nilai akurasi sebesar 97.77%, sehingga menurut peneliti ini masih bisa untuk di kaji lagi dengan menggunakan algortima yang berbeda. Sebagai proses pencarian informasi dari sekumpulan data yang akan dijadikan pengetahuan baru dapat dimanfaatkan maka dari itu data mining juga seringkali dikenal dengan sebutan Knowledge Discovery in Database (KDD). Metode klasifikasi yang digunakan yaitu Deep Learning H2O yaitu suatu metode menggunakan algoritma multilayer yang di sebut neural networks. Tujuan dari algoritma ini mencoba untuk mengambil suatu kesimpulan berdasarkan struktur logika yang di berikan secara berkelanjutan. Peneliti menggunakan software aplikasi Rapid Miner sebagai bantuan dalam menganalisis dataset. Dari hasil penelitian terbukti bahwa algoritma Deep Learning H2O yang digunakan lebih baik. Hal ini dibuktikan dengan hasil evaluasi penelitian bahwa algoritma Deep Learning H2O mampu menganalisa nilai recall 100.00% dan tingkat akurasi sebesar 99.66% sehingga model klasifikasi menggunakan algoritma Deep Learning H2O lebih baik saat diterapkan pada dataset yang digunakan
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Novaes, Matheus P., Luiz F. Carvalho, Jaime Lloret, and Mario Lemes Proença. "Adversarial Deep Learning approach detection and defense against DDoS attacks in SDN environments." Future Generation Computer Systems 125 (December 2021): 156–67. http://dx.doi.org/10.1016/j.future.2021.06.047.
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Elubeyd, Hani, and Derya Yiltas-Kaplan. "Hybrid Deep Learning Approach for Automatic Dos/DDoS Attacks Detection in Software-Defined Networks." Applied Sciences 13, no. 6 (March 16, 2023): 3828. http://dx.doi.org/10.3390/app13063828.
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This paper proposes a hybrid deep learning algorithm for detecting and defending against DoS/DDoS attacks in software-defined networks (SDNs). SDNs are becoming increasingly popular due to their centralized control and flexibility, but this also makes them a target for cyberattacks. Detecting DoS/DDoS attacks in SDNs is a challenging task due to the complex nature of the network traffic. To address this problem, we developed a hybrid deep learning approach that combines three types of deep learning algorithms. Our approach achieved high accuracy rates of 99.81% and 99.88% on two different datasets, as demonstrated through both reference-based analysis and practical experiments. Our work provides a significant contribution to the field of network security, particularly in the area of SDN. The proposed algorithm has the potential to enhance the security of SDNs and prevent DoS/DDoS attacks. This is important because SDNs are becoming increasingly important in today’s network infrastructure, and protecting them from attacks is crucial to maintaining the integrity and availability of network resources. Overall, our study demonstrates the effectiveness of a hybrid deep learning approach for detecting DoS/DDoS attacks in SDNs and provides a promising direction for future research in this area.
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Khasawneh, Natheer, Mohammad Fraiwan, Luay Fraiwan, Basheer Khassawneh, and Ali Ibnian. "Detection of COVID-19 from Chest X-ray Images Using Deep Convolutional Neural Networks." Sensors 21, no. 17 (September 3, 2021): 5940. http://dx.doi.org/10.3390/s21175940.
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The COVID-19 global pandemic has wreaked havoc on every aspect of our lives. More specifically, healthcare systems were greatly stretched to their limits and beyond. Advances in artificial intelligence have enabled the implementation of sophisticated applications that can meet clinical accuracy requirements. In this study, customized and pre-trained deep learning models based on convolutional neural networks were used to detect pneumonia caused by COVID-19 respiratory complications. Chest X-ray images from 368 confirmed COVID-19 patients were collected locally. In addition, data from three publicly available datasets were used. The performance was evaluated in four ways. First, the public dataset was used for training and testing. Second, data from the local and public sources were combined and used to train and test the models. Third, the public dataset was used to train the model and the local data were used for testing only. This approach adds greater credibility to the detection models and tests their ability to generalize to new data without overfitting the model to specific samples. Fourth, the combined data were used for training and the local dataset was used for testing. The results show a high detection accuracy of 98.7% with the combined dataset, and most models handled new data with an insignificant drop in accuracy.
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El Kamel, Ali, Hamdi Eltaief, and Habib Youssef. "On-the-fly (D)DoS attack mitigation in SDN using Deep Neural Network-based rate limiting." Computer Communications 182 (January 2022): 153–69. http://dx.doi.org/10.1016/j.comcom.2021.11.003.
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Zhang, Nan, Heikki Hämmäinen, and Hannu Flinck. "Cost efficiency of SDN-enabled service function chaining." info 18, no. 5 (August 8, 2016): 45–55. http://dx.doi.org/10.1108/info-03-2016-0011.
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Purpose This paper models the cost efficiency of service function chaining (SFC) in software-defined LTE networks and compares it with traditional LTE networks. Design/methodology/approach Both the capital expenditure (CAPEX) and operational expenditure (OPEX) of the SFC are quantified using an average Finnish mobile network in 2015 as a reference. The modeling inputs are gathered through semi-structured interviews with Finnish mobile network operators (MNO) and network infrastructure vendors operating in the Finnish market. Findings The modeling shows that software-defined networking (SDN) can reduce SFC-related CAPEX and OPEX significantly for an average Finnish MNO in 2015. The analysis on different types of MNOs implies that a MNO without deep packet inspection sees the biggest cost savings compared to other MNO types. Practical implications Service function investments typically amount to 5-20 per cent of the overall MNO network investments, and savings in SFC may impact highly on the cost structure of a MNO. In addition, SFC acts as both a business interface, which connects the local MNOs with global internet service providers, and as a technical interface, where the 3GPP and IETF standards meet. Thus, the cost efficient operation of SFC may bring competitive advantages to the MNO. Originality/value The results show solid basis of network-related cost savings in SFC and contributes to MNOs making cost conscious investment decisions. In addition, the results act as a baseline scenario for further studies that combine SDN with virtualization to re-optimize network service functions.
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Alonso, Ricardo S., Inés Sittón-Candanedo, Roberto Casado-Vara, Javier Prieto, and Juan M. Corchado. "Deep Reinforcement Learning for the Management of Software-Defined Networks and Network Function Virtualization in an Edge-IoT Architecture." Sustainability 12, no. 14 (July 15, 2020): 5706. http://dx.doi.org/10.3390/su12145706.
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The Internet of Things (IoT) paradigm allows the interconnection of millions of sensor devices gathering information and forwarding to the Cloud, where data is stored and processed to infer knowledge and perform analysis and predictions. Cloud service providers charge users based on the computing and storage resources used in the Cloud. In this regard, Edge Computing can be used to reduce these costs. In Edge Computing scenarios, data is pre-processed and filtered in network edge before being sent to the Cloud, resulting in shorter response times and providing a certain service level even if the link between IoT devices and Cloud is interrupted. Moreover, there is a growing trend to share physical network resources and costs through Network Function Virtualization (NFV) architectures. In this sense, and related to NFV, Software-Defined Networks (SDNs) are used to reconfigure the network dynamically according to the necessities during time. For this purpose, Machine Learning mechanisms, such as Deep Reinforcement Learning techniques, can be employed to manage virtual data flows in networks. In this work, we propose the evolution of an existing Edge-IoT architecture to a new improved version in which SDN/NFV are used over the Edge-IoT capabilities. The proposed new architecture contemplates the use of Deep Reinforcement Learning techniques for the implementation of the SDN controller.
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Mabel John, Prathima, and Rama Mohan Babu Kasturi Nagappasetty. "An intelligent system to detect slow denial of service attacks in software-defined networks." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 3 (June 1, 2023): 3099. http://dx.doi.org/10.11591/ijece.v13i3.pp3099-3110.
Full textAbstract:
<span lang="EN-US">Slow denial of service attack (DoS) is a tricky issue in software-defined network (SDN) as it uses less bandwidth to attack a server. In this paper, a slow-rate DoS attack called Slowloris is detected and mitigated on Apache2 and Nginx servers using a methodology called an intelligent system for slow DoS detection using machine learning (ISSDM) in SDN. Data generation module of ISSDM generates dataset with response time, the number of connections, timeout, and pattern match as features. Data are generated in a real environment using Apache2, Nginx server, Zodiac FX OpenFlow switch and Ryu controller. Monte Carlo simulation is used to estimate threshold values for attack classification. Further, ISSDM performs header inspection using regular expressions to mark flows as legitimate or attacked during data generation. The proposed feature selection module of ISSDM, called blended statistical and information gain (BSIG), selects those features that contribute best to classification. These features are used for classification by various machine learning and deep learning models. Results are compared with feature selection methods like Chi-square, T-test, and information gain.</span>