- Have any questions?
- +91 9176206235
- info@phdsupport.org
PhD in Detection of DDoS Attack Using Machine Learning
PhD in Lightweight Authentication based Cyber System
May 27, 2021PhD in Dos Attack Against UAV Communication System
May 27, 2021
PhD in Detection of DDoS Attack on SDN Control Plane Using Machine Learning
Software-defined networking (SDN) the weakness in the networks achieved by disassociating the control plane and allows the network to be efficiently programmable. Dental distribution attack is one of the significantly growing in recent attacks. It separates the core network’s logic control from the underlying routing and switching elements. DDoS attack prevents the authorized users alone to access the available resources at anytime based on The Detection of DDoS Attack on SDN control plane using machine learning.
RESEARCH APPROACH:
DDoS attacks are controlled by applying the proposed hybrid machine learning model where it provides more accuracy, detection rate, and false alarm rate compared to certain machine learning models. that the main function control plane is to install the following rules to the forwarding devices .the receiver operating character (ROC) curve to evaluate the model and it performs accurately. The detected malicious traffic can be blocked using null routing for further investigation and thus simulate the SDN network with various environments based on The Detection of DDoS Attack on SDN control plane using machine learning
LATEST ISSUE:
- Only selecting relevant features for a specific attack is not a possible solution due to various types of attacks occurring environment.
- The SDN network may affect various traditional attacks like spoofing, the elevation of privilege, information disclosure, and other issues also.
- It is hard to discover the execution of DDoS attacks using the bots devices.
- Feature selection in classical techniques needs experts to choose the proper features manually.
PROPOSED SOLUTION:
- The recurrent neural network (RNN) technique helps as a solution for control network traffic and for avoiding loss.
- To identify DDoS attacks and normal traffic and thus mitigate DDoS attacks, machine learning techniques will be used.
- Open flow protocol is used to enable secure communication between the SDN controller and the switch.
- To simulate DDoS attack detection that the generation of UDP flooding attack traffic and normal traffic is applied.
- On basis of the survey that the hybrid models may produce the high performance in terms of false and accuracy rate.
FUTURE PROPOSAL:
- In this proposal “The Detection of DDoS Attack on SDN control plane using machine learning” SVM algorithm based ML techniques and binary classification, framework is utilized to classify the input traffic into normal and malicious type.
- In The future, the proposed“The Detection of DDoS Attack on SDN control plane using machine learning” model is to be tested on basis of its test performance on other datasets.
Our experts provide complete guidance for PhD in Detection of DDoS Attack on SDN control plane using machine learning.
