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Abstract
In today’s world, when ubiquitous computing has become prevalent, there has been an upsurge in the number of users on the internet. The Distributed Denial of Service attack is the most widespread attack that disrupts the functioning of websites, servers, and services. In such attacks, the resources of the target are exhausted by overwhelming requests from multiple attackers, and thus become unavailable to the users. Hence, it is essential to detect these attacks and prevent network security breaches. This work presents a machine learning based DDoS detection system using machine learning algorithms. The system was developed using the CICIDS 2017 dataset. Various statistical methods are used to generate the data, and then feature selection and classification models are applied to it. The accuracies of various models, along with other metrics, are compared to select and analyze the algorithm for detection of the DDoS attacks.