A Machine Learning Approach for DDoS Attack Detection in CIC-DDoS2019 Dataset Using Multiple Linear Regression Algorithm

In the context of cloud computing, distributed denial-of-service (DDoS) attacks are malicious attempts to overwhelm a cloud service, network, or an application with a huge flow of traffic from several unidentified sources, rendering it inaccessible to consumers. In cloud computing systems, where many users share the common underlying infrastructure, DDoS attacks are the most common and destructive types of cyber threats. The attackers’ motive is to hinder and halt the services of applications by posing a stream of illegitimate traffic which is achieved with the help of botnets also known as compromised computers that are infected with certain types of malwares such as viruses or trojans. These botnets can be controlled by the attackers, and the malign traffic is generated by various techniques like IP spoofing to make it more challenging to identify the source of the attack. DDoS attacks can be carried out for a variety of reasons, including monetary gain, social or political action, personal grudges, or just the desire to wreak havoc and disorder.The main objective of this chapter is to propose a model capable of anticipating and detecting the percentage of DDoS attacks using multiple linear regression, a dimensionality reduction approach using feature selection by taking the CIC-DDoS 2019 dataset, and compare the classifier result before and after dimensionality reduction using feature selection with multiple linear regression.