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Academic literature on the topic 'Software Defined Networking (SDN) / OpenFlow'
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Journal articles on the topic "Software Defined Networking (SDN) / OpenFlow"
1
Alotaibi, Modhawi, Ahmed Helmy, and Amiya Nayak. "Modeling Handover Signaling Messages in OpenFlow-Based Mobile Software-Defined Networks." Journal of Computer Networks and Communications 2018 (December 18, 2018): 1–14. http://dx.doi.org/10.1155/2018/1543531.
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The software-defined networking (SDN) paradigm has become essential in tackling several issues and challenges in conventional networking, especially in mobile/cellular networks. In order to realize the benefits brought by SDN to mobility management, we study the effects of SDN in conjunction with OpenFlow protocol on the handover procedure. However, in this new setting, the handover still suffers from delay due to the exchange of OpenFlow signaling messages. In this paper, we focus on SDN in mobile networks and quantify the delays of handover-related OpenFlow messages in order to identify the performance measures as well as the underlying challenges. For our analysis, we provide an analytical model, using which we modeled two handover-related OpenFlow messages in such networks. To the best of our knowledge, no previous work has modeled OpenFlow messages other than Packet-in messages. In this paper, in addition to the Packet-in message, we model Port-status messages. Following our analysis, we propose a novel solution to make handover more efficient and less interruptive. Furthermore, we study our solution in an LTE architecture and compare it to an existing solution. We show that, in normal traffic conditions, our solution can decrease the handover delay as much as 20%.
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Allouzi, Maha Ali, and Javed I. Khan. "Advanced Authentication Protocol for Software-Defined Networks." International Journal of Semantic Computing 12, no. 03 (2018): 361–71. http://dx.doi.org/10.1142/s1793351x18400160.
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Software-Defined Networking (SDN) is being widely implemented by critical infrastructure networks, however providing security features still a challenge. In this work, we present SafeFlow an Automatic Trust Negotiation Protocol for SDN, a first line of defense and fine-grained authentication protocol, in order to deny the access of OpenFlow switches without valid digital credentials. Traditional security approaches based on identity or capabilities do not solve the problem of establishing trust between strangers. One alternative approach to mutual trust establishment is Trust Negotiation, the bilateral exchange of digital credentials to establish trust gradually. The proposed protocol describes Trust Negotiation in OpenFlow protocol, probable extension to the OpenFlow handshake protocol. In this paper, we describe the implementation of SafeFlow. The proposed protocol ensures the security of the infrastructure itself, as there are also other proposals for developing security application on OpenFlow network infrastructure.
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Bayu, Teguh Indra, and Etvan Ewaldo Tahan. "SOFTWARE DEFINED NETWORK (SDN) SIMULATION CONCEPT USING RASPBERRY PI." Jurnal Terapan Teknologi Informasi 2, no. 2 (2018): 1–11. http://dx.doi.org/10.21460/jutei.2018.22.100.
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In school environment, office, company or society, needs a computer network which uses for supporting every users’ working and to communicate each other. However, it makes some users who are not responsible could able to disturb communication security system data in networking. Security system is needed to avoid the problems. Access Control List (ACL) can be an alternative to secure a computer network. By applying Software Defined Network (SDN) concept which is based on management concept of centralized network use software. It focus on how applying ACL in infrastructure network SDN use Raspberry Pi as an openflow switch. The decisive element of an infrastructure network SDN is a controller, an openflow switch, and network device would connect one network to the other network by using Openflow protocol. From the research which is conducted SDN concept, ACL able to applied using flow-control method. It gives some clear value on flow-control, so the restriction access can be done in every network device which is connected in infrastructure network SDN.
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Kareem, Mohammed Ibrahim, and Mahdi Nsaif Jasim. "Entropy-based distributed denial of service attack detection in software-defined networking." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 3 (2022): 1542. http://dx.doi.org/10.11591/ijeecs.v27.i3.pp1542-1549.
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Software defined networking (SDN) is a new network architecture that allows for centralized network control. The separation of the data plane from the control plane, which establishes a programmable network environment, is the key breakthrough underpinning SDN. The controller facilitates the deployment of services that specify control policies and delivers these rules to the data plane using a common protocol such as OpenFlow at the control plane. Despite the many advantages of this design, SDN security remains a worry because the aforementioned chapter expands the network's attack surface. In fact, denial of service (DoS) assaults pose a significant threat to SDN settings in a variety of ways, owing to flaws in the data and control layers. This work shows how distributed denial of service (DDoS) attack detection is based on the entropy variation of the destination IP address. The study takes advantage of the OpenFlow protocol's (OFP) flexibility and an OpenFlow controller (POX) to apply the proposed method. An entropy computation to determine the distributed features of DDoS traffic is developed and it is capable of detecting a user datagram protocol (UDP) flood attack after 0.445 seconds this type of attack occurred.
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Savaliya, Abhishek, Rutvij H. Jhaveri, Qin Xin, Saad Alqithami, Sagar Ramani, and Tariq Ahamed Ahanger. "Securing industrial communication with software-defined networking." Mathematical Biosciences and Engineering 18, no. 6 (2021): 8298–313. http://dx.doi.org/10.3934/mbe.2021411.
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<abstract> <p>Industrial Cyber-Physical Systems (CPSs) require flexible and tolerant communication networks to overcome commonly occurring security problems and denial-of-service such as links failure and networks congestion that might be due to direct or indirect network attacks. In this work, we take advantage of Software-defined networking (SDN) as an important networking paradigm that provide real-time fault resilience since it is capable of global network visibility and programmability. We consider OpenFlow as an SDN protocol that enables interaction between the SDN controller and forwarding plane of network devices. We employ multiple machine learning algorithms to enhance the decision making in the SDN controller. Integrating machine learning with network resilience solutions can effectively address the challenge of predicting and classifying network traffic and thus, providing real-time network resilience and higher security level. The aim is to address network resilience by proposing an intelligent recommender system that recommends paths in real-time based on predicting link failures and network congestions. We use statistical data of the network such as link propagation delay, the number of packets/bytes received and transmitted by each OpenFlow switch on a specific port. Different state-of-art machine learning models has been implemented such as logistic regression, K-nearest neighbors, support vector machine, and decision tree to train these models in normal state, links failure and congestion conditions. The models are evaluated on the Mininet emulation testbed and provide accuracies ranging from around 91–99% on the test data. The machine learning model with the highest accuracy is utilized in the intelligent recommender system of the SDN controller which helps in selecting resilient paths to achieve a better security and quality-of-service in the network. This real-time recommender system helps the controller to take reactive measures to improve network resilience and security by avoiding faulty paths during path discovery and establishment.</p> </abstract>
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Yujie Xie and Pankoo Kim. "Novel privacy vulnerabilities and challenges of OpenFlow-based SDN in network security." Research Briefs on Information and Communication Technology Evolution 3 (October 15, 2017): 84–90. http://dx.doi.org/10.56801/rebicte.v3i.47.
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In the last decade, software defined networking (SDN) with novel mobile network environment hasdrawn so many attentions from both IT industries and academic because of its huge convenient andeconomic cost. Advanced mobile networks provide abundant entertainments and rich lifestyle forcitizens, which requires larger storage and higher quality of internet environments such as innovativemobile cloud computing and especially potential SDN [1]. Then emerging SDN technology satisfyingmore users’ demands gains a lot of momentum, which is a new network structure with highersecurity than traditional networks. Meanwhile, novel vulnerabilities and challenges play the mostimportant roles in the deployment of software defined networking. In this paper, at first, we brieflyintroduce software defined networking including three main planes. Then OpenFlow-based SDN areillustrated. The procedure of OpenFlow-based SDN working is also provided in this part. And relatedsecurity vulnerabilities and challenges on each layer are presented in the last part.
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Wang, Rui, Zhiyong Zhang, Lei Ju, and Zhiping Jia. "A Novel OpenFlow-Based DDoS Flooding Attack Detection and Response Mechanism in Software-Defined Networking." International Journal of Information Security and Privacy 9, no. 3 (2015): 21–40. http://dx.doi.org/10.4018/ijisp.2015070102.
Full textAbstract:
Software-Defined Networking (SDN) and OpenFlow have brought a promising architecture for the future networks. However, there are still a lot of security challenges to SDN. To protect SDN from the Distributed denial-of-service (DDoS) flooding attack, this paper extends the flow entry counters and adds a mark action of OpenFlow, then proposes an entropy-based distributed attack detection model, a novel IP traceback and source filtering response mechanism in SDN with OpenFlow-based Deterministic Packet Marking. It achieves detecting the attack at the destination and filtering the malicious traffic at the source and can be easily implemented in SDN controller program, software or programmable switch, such as Open vSwitch and NetFPGA. The experimental results show that this scheme can detect the attack quickly, achieve a high detection accuracy with a low false positive rate, shield the victim from attack traffic and also avoid the attacker consuming resource and bandwidth on the intermediate links.
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Kartadie, Rikie. "MIKROTIK RB750 ROUTERBOARD SEBAGAI ALTERNATIF SWITCH OPENFLOW SOFTWARE-BASE." Simetris : Jurnal Teknik Mesin, Elektro dan Ilmu Komputer 7, no. 2 (2016): 467. http://dx.doi.org/10.24176/simet.v7i2.756.
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Software-Defined Networking (SDN) merupakan cara untuk mengurangi kompleksitas konfigurasi jaringan dan manajemen jaringan. Solusi yang ditawarkan oleh SDN memberikan nuansa baru pada jaringan komputer. Pengimplementasian arsitektur SDN/OpenFlow membutuhkan biaya yang tinggi, sedangkan penggunaan emulator mininet mampu memberikan simulasi yang baik pada skala penelitian, namun dalam kenyataannya implementasinya membutuhkan hardware. Pengimplementasian OpenFlow pada switch telah merambah kebeberapa vendor, diantaranya MikroTik yang telah menambahkan OpenFlow agent pada OS versi 6.17 pada RouterOS nya dan memungkinkannya untuk dapat diimplementasikan pada arsitektur SDN/OpenFlow dengan biaya yang lebih terjangkau. Implementasi OpenFlow agent pada RouterOS MikroTik layak untuk diuji performanya, sebagai alternatif switch OpenFlow software-base. Penelitian ini adalah penelitian awal dari rangkaian penelitian yang akan dilakukan. Langkah-langkah penelitian adalah: (1)Merancang topologi, (2)Simulasi menggunakan emulator mininet sebagai data pembanding (dianggap sebagai representatif dari hardware-base switch), (3)Memodifikasi MikroTik RB750 sebagai prototipe switch OpenFlow software-base, (4) Pengujian prototipe, (5) Analisis hasil dan menarik kesimpulan. Dari hasil uji latency, prototipe memberikan nilai lebih tinggi dibandingkan dengan nilai pembandingnya, nilai throughput TPC dan UDP 1937.5 kbps dan 8.64 kbps dan nilai jitter 0.0093 msec lebih rendah dibandingkan nilai pembandingnya. Prototipe dapat dijadikan alternatif pengganti switch OpenFlow Software-based, walaupun prototipe masih memberikan nilai performa yang rendah. Kata kunci: mininet, mikrotik, software-defined network, openflow.
9
Shrivastava, Gourav, Praveen Kaushik, and R. K.Pateriya. "Load balancing strategies in software defined networks." International Journal of Engineering & Technology 7, no. 3 (2018): 1854. http://dx.doi.org/10.14419/ijet.v7i3.14017.
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In the past few years, network requirements have been changing frequently as the amount of data traffic increasing exponentially so it is difficult to utilize the full capacity of network resources. Software Defined Networking (SDN) is emerging as a new networking technology which decouples the control plane from the data plane in the network devices. Separation of control and data plane allows a network administrator a better control over network management and also enables new development through network programmability. Presently Open-Flow is the most popular SDN protocol which provides communication between network devices and controller. In this paper, the Round Robin algorithm is compared with the Dynamic load balancing algorithm using the OpenFlow protocol in SDN under varying load conditions of TCP and UDP traffic. Experimental analysis shows that the dynamic load balancing strategy works better than the Round Robin load balancing.
10
Elloumi Zitouna, Imene. "Learning-based Orchestrator for Intelligent Software-defined Networking Controllers." International Journal of Software Engineering & Applications 11, no. 6 (2020): 17–30. http://dx.doi.org/10.5121/ijsea.2020.11602.
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This paper presents an overview of our learning-based orchestrator for intelligent Open vSwitch that we present this using Machine Learning in Software-Defined Networking technology. The first task consists of extracting relevant information from the Data flow generated from a SDN and using them to learn, to predict and to accurately identify the optimal destination OVS using Reinforcement Learning and QLearning Algorithm. The second task consists to select this using our hybrid orchestrator the optimal Intelligent SDN controllers with Supervised Learning. Therefore, we propose as a solution using Intelligent Software-Defined Networking controllers (SDN) frameworks, OpenFlow deployments and a new intelligent hybrid Orchestration for multi SDN controllers. After that, we feeded these feature to a Convolutional Neural Network model to separate the classes that we’re working on. The result was very promising the model achieved an accuracy of 72.7% on a database of 16 classes. In any case, this paper sheds light to researchers looking for the trade-offs between SDN performance and IA customization.