SDN-Enabled IoT to Combat the DDoS Attacks

The ubiquitous growth in the Internet of Things has made network security the main concern nowadays. The Internet of Things is facing various network attacks which disrupt the network resources. The network technologies used to mitigate such attacks need to be improvised to combat the recent attacks. Thus, we have proposed an approach for detecting and mitigating DDoS network attacks using software-defined network (SDN) and machine learning. SDN is a network paradigm that is used to monitor and control the network traffic that helps to manage the network resources. In the proposed approach, we have used three machine learning models, namely Naive Bayes, logistic regression, and random forest to classify the network traffic, and after that, the SDN controller will update the network settings accordingly to mitigate the attack. The traffic generated from the applications is classified by the machine learning classifier models into benign and malicious categories. Then the classified results will be sent to the SDN controller which will either block the attacking traffic or update the network settings to combat it.