Software Defined Network Research Topics

Software Defined Networks research topics are proposed in this research and it is now widely employed in many applications. It is utilized to generate a network that is easy and flexible to manage. Now, here we offer the SDN based networking techniques:

  1. Define Software Defined Networks (SDN)

The first stage of every research must contain the definition; here also we see the definition for SDN technology. It is a structure planned to create a network that is simpler and flexible to handle. It concentrates on administration to extract the data plane through the transmit function of data in the distinct device for networking.

  1. What is SDN?

Once we have finished the definition, next we see the in-depth explanation of SDN. It is a technology for networking that employs “Application Programming Interfaces (APIs) or Software-based controllers” to interact with essential hardware framework and direct network based traffic.

  1. Where SDN used?

Next to the in-depth explanation we note where to utilize the SDN network. It is employed to explain the new technology difficulties like mobility, reducing power consumption, actual-time interaction, measurement and monitoring, Security, Traffic engineering and data center networking.

  1. Where SDN technology proposed? , Previous technology issues

We propose a SDN technology that is much more flexible than the networking on the basis of conventional, because it is software-based control plane method. It permits the managers to manage the system, change structure surroundings, enhance network capacity and Provision resources – entirely from a concentrated experience on user, excluding the requirement for more hardware. Some of the existing technology issues may contain Limited Network Programmability, Inefficient Resource Utilization and Lack of Network Flexibility and Agility.

  1. Algorithms / Protocols

For our proposed research we include the following methods / algorithms to overcome the existing technology. The algorithms that we used are Dove Swarm Optimization (SCO), Unique Net, K-means Clustering (KMC) algorithm, Improved Mersenne Twister (IMT) algorithm, Improved Deep Q Network (ImDQN) and Enhanced Bidirectional Generative Adversarial Network (E-BiGAN).

  1. Comparative Study / Analysis

Now we see the comparative study for this paper, this section compares the methods with existing techniques to find the best method for this research. The methods that we compare are as follows:

  • An Improved Deep Q Network (IDQN) is utilized to acquire from an increased amount of data and can solve complicated issues and then it is comparatively easy to execute.
  • The K-Means will find and detect hiding patterns in a dataset and this also identifies the framework and arrangement of data by selecting automatically because of its centroid to make it a creative up to date mechanism and repeated assignment.
  • For imbalanced parameters during the tunnel construction an Improved BIGAN method is used.
  1. Simulation Results / Parameters

Our proposed method is evaluated by comparing the various performance metrics or parameters to acquire a best finding. The methods that we compared are Slice Capacity, Packet Transmission, Accuracy, Bandwidth Consumption, Latency, Throughput and Packet Loss Rate.

  1. Dataset LINKS / Important URL

In this research we propose a SDN b based networking technology that is widely used in many places and it is proposed to overcome several previous technology issues with the help of below provided links:

  1. SDN Applications

Let’s see the applications to be used for this research SDN. It is the most extensively utilized method for application deployment, to arrange applications rapidly while decreasing the whole operation rate and deployment. SDN permits IT managers to handle and offer network services in a single position.

  1. Topology for SDN

The general demonstration of the SDN framework consists of three layers: the infrastructure layer, control layer and the application layer. These layers will interact with the southbound and northbound Application Programming Interfaces (APIs).

  1. Environment in SDN

The environment to be utilized for this research is as follows. SDN is a technique on networking which utilizes APIs or Software – based controllers to interact with the essential direct traffic over network and hardware framework.

  1. Simulation tools

In this research of SDN based networking we employ the following simulation setup or software requirements as follows. The tool here we used to execute the work is python 2.7 or Above Version and Mininet 2.0 or Above Version. Then the wireshark is used when it is needed. Moreover our proposed work is implemented by using the programming language Python. Then the operating system that is needed for this research is Ubuntu 14.04 LTS (32 bit) or Above Version.

  1. Results

SDN based networking is proposed in this research and it addresses several previous technology issues. In this research we compared various metrics or parameters to achieve that our proposed gives the best accuracy. Then it is implemented by using the Python programming language to obtain the best findings.

Software Defined Networks Research Ideas:

SDN technology is proposed in this research; here we are given some research topics that are relevant to our proposed SDN network. These topics give such important information about SDN and are listed as follows.

  1. Research on Network Application of Intelligent Campus Based on SDN Technology
  2. SDN Network Reliability Guarantee Mechanism based on Network Characteristics
  3. Archipelago: A Hybrid Multi-Node Campus SDN Architecture
  4. Flexible Wired/Wi-Fi TSN Networking Through SDN and Soft Traffic Control
  5. Research on SDN Network Structure Optimization System Based on Computer 5G Technology
  6. Detection of Attacks in Software-Defined Networks (SDN)* : *How to conduct attacks in SDN environments
  7. SDN Dynamic Controller Configuration to Mitigate Compromised Controllers
  8. Communication Networks with Multiple SDN Controllers
  9. Collaborative D2D Cache System Based on SDN Network
  10. SDN Lullaby: VM Consolidation for SDN using Transformer-Based Deep Reinforcement Learning
  11. Performance Analysis of SDN with a Hybrid Data Plane of MPLS and SDN
  12. AID-SDN: Advanced Intelligent Defense for SDN Using P4 and Machine Learning
  13. On the (in)Security of the Control Plane of SDN Architecture: A Survey
  14. A Dynamic VNF Deployment to Avoid Controller Overload in SDN-Cluster
  15. Experimenting SDN/NFV Solutions for Flexible Maritime Transport & Logistics (T&L) Services
  16. An Effective, Efficient and Scalable Link Discovery (EESLD) Framework for Hybrid Multi-Controller SDN Networks
  17. Deep Learning Model to Defend against Covert Channel Attacks in the SDN Networks
  18. BOPIS-Software: A Python based Software application for SDN East-West inter Autonomous System communication and Programmable Network Monitoring tool
  19. ITor-SDN: Intelligent Tor Networks Based SDN for Data Forwarding Management
  20. FBA-SDN: A Federated Byzantine Approach for Blockchain-Based Collaborative Intrusion Detection in Edge SDN
  21. DDoS Vulnerabilities Analysis in SDN Controllers: Understanding the Attacking Strategies
  22. Machine Learning Algorithms for Enhancing Intrusion Detection Within SDN/NFV
  23. Evaluating SDN Applicability in the Edge
  24. Seamless Integration of Wireless Interface to SDN Switch
  25. Intrusion Detection System for SDN based VANETs Using A Deep Belief Network, Decision Tree, and ToN -IoT Dataset
  26. Security Assessment of Low Earth Orbit (LEO) with Software-Defined Networking (SDN) Structure
  27. Physical Assessment of an SDN-Based Security Framework for DDoS Attack Mitigation: Introducing the SDN-SlowRate-DDoS Dataset
  28. AI based DDOS Attack Detection of SDN Network in Mininet Emulator
  29. SDN Enabled Big Data Analytics and Framework for Sensor Data of Vehicle Health, Safety and Monitoring System
  30. Resilient Control-Plane Design for T-SDN Based Optical Transport Networks
  31. A Proposed Cryptography Key Management in Software-Defined Networking (SDN)
  32. Control Plane TCP Attacks Detection and Prevention in SDN Networks using Deep Learning Model
  33. Enhancing Resilience against DDoS Attacks in SDN -based Supply Chain Networks Using Machine Learning
  34. Using SDN to Enhance Load Balancing in Cloud Computing: An Overview and Future Directions
  35. Application of SDN Technology in Data Center Network Construction and Room Migration
  36. Identification of DDoS Attack on IoT Network Using SDN
  37. SDN-Based LDoS Mitigation System
  38. Security Enhancement through Flow-based Centralized control in SDN
  39. Research on System Application of Network Wireless Transmission Technology Based on Computer SDN
  40. Leveraging SDN for Real World Windfarm Process Automation Architectures
  41. Research on the Design of SDN Controller Based on Campus Network
  42. Disjoint Multipath Routing for Naval SCADA Systems by Utilizing RYU SDN Controller
  43. Research on Operation and Maintenance Management System of Data Center SDN Network
  44. Implementation of a Traffic Recognition and Reconstruction Technology Based on SDN Networks
  45. Streaming via SDN: Resource forecasting for video streaming in a Software-Defined Network
  46. POXIEM: An ELK Integrated SDN Controller Proposal for Improved Control Plane Forensic Visibility and Incident Response
  47. SDN-based network virtualization resource management architecture and mapping algorithm
  48. Traffic-Adaptive Scheme for SDN Control Plane with Containerized Architecture
  49. Prediction of Distributed Denial of Service Attacks in SDN using Machine Learning Techniques
  50. Event Driven State Transition Schedulers for SDN-IoT architecture based on 6LoWSD protocol and its energy evaluation on the QPN model