SDN based attack detection in IoT Research Topics

SDN based attack detection in IoT research topic is now widely utilized to enhance network security. Different methodologies and parameters are analyzed in this research to get a strong consequence. Below we have provided some SDN based attack detection concepts, applications, methods and parameters.

  1. Define SDN based attack detection in IoT?

At the beginning we first take a look at the definition of SDN based attack detection in IoT, this is a networking method that can be divide control plane from the data plan; centralized network management and programing are allowed. SDN -based machine learning technology is used to improve the security in attack detection for IOT environments. For anomalies detection, network traffic patents, and identifies potential threats in IoT devices this technique suggests a machine learning algorithm. Security guidelines may be dynamically modified and implemented in real time depending on the risks identified by incorporating machine learning models into the SDN architecture. For growing cyber threats, reducing potential vulnerabilities, and improving the total resilience of the network to this active strategy improves the security of IoT.

  1. What is SDN based attack detection in IoT?

Here we can discuss about the detailed explanation of attack detection in IOT based on SDN, IoT ecosystems combined with machine learning provides a powerful attack detection solution. By dividing control plane and data planes, SDN make dynamic network organization, centralized control of network traffic are allowed. It can able to identify patterns anomalies detection, and predict potential threats in IoT environments, when integrated with machine learning algorithms. For examining massive amounts of data produced by IoT devices, quickly analyze trigger responsive actions and, abnormal behaviors this approach, prevent attacks from rerouting traffic or separate compromised devices.  Machine learning and SDN mechanisms are used to improve the threat detection and response for IoT security in the real world.

  1. Where SDN based attack detection in IoT used?

Where does SDN based attack detection is used is a crucial factor to discuss, let’s see about it, SDN is used in different industries and network environments to improve flexibility, scalability, and manageability. It is utilized in data centers to optimize resource utilization, configurations; administrators to centrally manage network traffic are allowed. To enhance the efficacy of the networks for telecommunication companies are used SDN, for better control over routing and traffic, prioritization is allowed. This method is also used in initiative networks, enabling, security improvements, dynamic adjustments and easier combination of new services or applications. For healthcare, education, and finance zones, using this valuable method for networks requires improved security measures, agility, and robustness.

  1. Why SDN based attack detection in IoT proposed? Previous technology issues

SDN based machine learning technology for attack detection on cyber security in IoT environments the main limitation is a single controller failure in SDN and high dimensional data issue. The limitation can be increased training time and low detection capability. The major limitations can be scalability challenges and success monitoring in high dimensional data, increased training time, limited detection capability, scalability issues and performance constraints.

  1. Algorithms/ protocols

The SDN-based attack detection in IoT suggested in this work and it overcomes the limitations, we provide some approaches or techniques to be utilized for attack detection on cybersecurity in IoT Mix-Max normalization, whale optimization algorithm – fisher feature importance based singular value decomposition, unified and standalone monitoring module with bi-objective optimization, stacked sparse unto encoder with support vector machine based median fitness oriented sea line optimization algorithm, partial rank correlation-based detection, policy enforcement module, proof -of -authority  of the methodologies used in attack detection on cybersecurity in IoT devices.

  1. Comparative study / Analysis

Following the algorithms or protocols to be utilized in our work, we have to compare several techniques to analyze the corresponding outputs; here we provide some technologies to be compared are mentioned below:

  • For data pre-processing, we suggest “Min-Max normalization”. To reduce the loss function and vary scale ranges during training.
  • For feature selection and extraction, we introduce Whale Optimization Algorithm-Fisher Feature Importance-based Singular Value Decomposition (WOA-F2I-SVD)”. This method is used for selecting features, reducing a dimension vector on optimal subsets, and high dimensional data of the features that can be selected and extracted in the new space are used for the singular value decomposition algorithm.
  • We have using combined a unified and standalone monitoring module with a bi-objective optimization (USMM-BOO) for network traffic monitoring, this method enhances scalability from all traffic network also monitoring data is normal or malicious. USMM is used for dividing the function of monitoring from new parts of the control and management plane.
  • For attack detection is normal or malicious we have introduce machine learning based stacked sparse autoencoder with support vector machine based Median Fitness oriented Sea Lion Optimization” SSAE-SVM-MFSLnO algorithm it is processed in real-time, and advanced efficiency.
  • We introduced Partial Rank Correlation-based Detectionapproach for identify high and low rate DDOs on non-volumetric assaults.
  • MFSLnO is combined in this method to enhance the performance.
  • Policy-Enforcement Module mitigation approach is used for security based attack detection.
  • Proof-of-Authority (PoA) Blockchain consensus protocol used to evaluate data efficiency for minimize latency, complexity, energy efficiency, and IoT transactions.
  1. Simulation results / Parameters

Successfully achieving the comparative analysis, we have to compare varies parameters for the SDN based attack detection in IoT environment to find consistent outcome.

For SDN based attack detection on cybersecurity in IoT devices we compare the parameter like accuracy, time, sparse parameter, attack rate, and available energy these all parameters were compared with Number of users, load, train time, packet loss ratio and valid blocks these are the parameters that we compared to find the greatest outputs.

  1. Dataset LINKS / Important URL

In this the parameters we selected are compared to obtain the best outcomes, and then afterwards we have provide some important links here that is very useful to summary the SND based attack detection on cybersecurity in IoT environment uses, application and some additional references for any explanation we use through the following links;

  1. SDN based attack detection in IoT Applications

We have provide some application for SDN based attack detection in IoT like data center networking, network function virtualization, network security, 5G networks, traffic engineering and QoS, software define-WAN  are some of the applications to be employed in SDN based attack detection in IoT.

  1. Topology for SDN based attack detection in IoT

SDN based attack detection on cybersecurity in IoT environment provide some topology like data collection, data preprocessing, feature selection and extraction, network traffic monitoring, attack detection, security based attack mitigation, block chain based data storage which utilize service to activate in IoT.

  1. Environment in SDN based attack detection in IoT

We discuss the SDN based attack detection in IoT environment, SDN make dynamic network organizations, centralized control of network traffic are allowed. For anomalies detection or malicious patterns, a machine-learning algorithm used, for a network requires robustness, agility, and improved security measures in an IoT environment.

  1. Simulation tools

The proposed system requires the subsequent software requirements. We require that the SDN based attack detection in IoT is to implement this work by incorporating the language C++ and it is developed by the tool NS 3.26. The operating system that is required for the work is Ubuntu 16.06 [LTS] or above version. These are all the software requirements that we utilized for SND attack detection in IoT.

  1. Results

SND-based attack detection in IoT is to detect any malicious or attacks; we have suggested in this research to overcome the previous problems or issues. In this we compared different approaches to analyze and utilize different parameters to find the perfect output for this research. The software requirements that need to be implemented the research is C++ and the tool is NS 3.26.

SDN based attack detection in IoT Research Ideas:

The research topics listed below are our recommendations for gathering inspiration from this. This is helpful to us since it goes over the ideas, justifications, strategies, and tactics related to cyber securities that were used for that study.

  1. Lightweight Sybil Attack Detection in IoT based on Bloom Filter and Physical Unclonable Function
  2. Towards a machine learning-based framework for DDOS attack detection in software-defined IoT (SD-IoT) networks
  3. Attack detection analysis in software-defined networks using various machine learning method
  4. Ddos attack detection approaches in on software defined network
  5. Towards Software-Defined Networking-based IoT Frameworks: A Systematic Literature Review, Taxonomy, Open Challenges and Prospects
  6. Security Framework for Internet-of-Things-Based Software-Defined Networks Using Blockchain
  7. Network intrusion detection in software defined networking with self-organized constraint-based intelligent learning framework
  8. Nature-inspired intrusion detection system for protecting software-defined networks controller
  9. Detection methods for software defined networking intrusions (SDN)
  10. MCAD: A Machine learning based cyberattacks detector in Software-Defined Networking (SDN) for healthcare systems
  11. An efficient hybrid-dnn for ddos detection and classification in software-defined iiot networks
  12. FMDADM: A Multi-Layer DDoS Attack Detection and Mitigation Framework Using Machine Learning for Stateful SDN-Based IoT Networks
  13. SDIWSN: A software-defined networking-based authentication protocol for real-time data transfer in industrial wireless sensor networks
  14. Capturing low-rate Ddos attack based on Mqtt protocol in software defined-Iot environment
  15. Detecting flooding DDoS attacks in software defined networks using supervised learning techniques
  16. Towards security automation in software defined networks
  17. A software defined networking architecture for ddos-attack in the storage of multimicrogrids
  18. Machine learning for detecting security attacks on blockchain using software defined networking
  19. A reinforcement learning based routing protocol for software-defined networking enabled wireless sensor network forest fire detection
  20. Reduction of the Delays Within an Intrusion Detection System (IDS) Based on Software Defined Networking (SDN)
  21. Software-defined DDoS detection with information entropy analysis and optimized deep learning
  22. Traffic flow monitoring in software-defined network using modified recursive learning
  23. ADAM: an adaptive DDoS attack mitigation scheme in software-defined cyber-physical system
  24. TD-RA policy-enforcement framework for an SDN-based IoT architecture
  25. Towards SDN-enabled, intelligent intrusion detection system for internet of things (IoT)
  26. Deep active learning intrusion detection and load balancing in software-defined vehicular networks
  27. IDPS-SDN-ML: An Intrusion Detection and Prevention System Using Software-Defined Networks and Machine Learning
  28. A flow-based anomaly detection approach with feature selection method against ddos attacks in sdns
  29. Securing IoT and SDN systems using deep-learning based automatic intrusion detection
  30. A novel approach for distributed denial of service defense using continuous wavelet transform and convolutional neural network for software-defined network
  31. Real-time Link Verification in Software-Defined Networks
  32. State of the art for edge security in software-defined networks
  33. Automatic, verifiable and optimized policy-based security enforcement for SDN-aware IoT networks
  34. DeepAir: Deep reinforcement learning for adaptive intrusion response in software-defined networks
  35. ADMS: An online attack detection and mitigation system for LDoS attacks via SDN
  36. Extended data plane architecture for in-network security services in software-defined networks
  37. Blockchain and deep learning for cyber threat-hunting in software-defined industrial IoT
  38. Multitentacle federated learning over software-defined industrial internet of things against adaptive poisoning attacks
  39. Towards a secure software defined network with adaptive mitigation of dDoS attacks by machine learning approaches
  40. Intelligent requests orchestration for microservice management based on blockchain in software defined networking: A security guarantee
  41. A Cost-Effective MTD Approach for DDoS Attacks in Software-Defined Networks
  42. DeepPlace: Deep reinforcement learning for adaptive flow rule placement in software-defined IoT networks
  43. Privacy preservation and security management in VANET based to Software Defined Network
  44. A framework for policy inconsistency detection in software-defined networks
  45. A hybrid deep learning approach for bottleneck detection in IoT
  46. An Exploration Into Secure IoT Networks Using Deep Learning Methodologies
  47. Application of Federated Learning in Health Care Sector for Malware Detection and Mitigation Using Software Defined Networking Approach
  48. Toward the protection of iot networks: Introducing the latam-ddos-iot dataset
  49. ML-based IDPS enhancement with complementary features for home IoT networks
  50. A feedforward–convolutional neural network to detect low-rate dos in iot