The main objective of the research is to provide an Intrusion Detection System which can detect the DDoS attacks using Machine Learning Algorithms by identifying the
suspicious packets, dropping them and sending a notification about the activity to all the network connected authorities. Organizations will be able to provide secure
communication and risk-free experience with a well-secured IoT environment through the proposed system. Most of the networks and IoT devices are very difficult to
maintain because of the lack of security issues and lack of knowledge on these devices. It is required to have a proper mechanism to protect the network and interconnected
IoT devices with no intruder disruptions. Along with it, there should be a mechanism to maintain the data privacy of the organization and the employees. Most of the time
organizations must pay a huge amount of money to hire an expert to configure the network and it takes more time to get the outcome. The proposed system
‘WANHEDA’ will be able to adapt to the network and do the needed configurations by itself. It will reduce the number of false alarms and increase the accuracy of the
network by giving a profitable financial benefit to the organizations.
Volumetric Distributed Denial of Service attack is one of the severe malicious attack which can be seen on Internet and it is responsible for more than half of all kinds of those attacks.
This proposal approach makes induction about how to detect volumetric attacks using machine learning and to make a safe environment for the users without DDoS disruption.
