SI22: A dataset for analysis of DoS attack on the Cloud

Abstract

Distributed Denial of Service (DDoS) is an attack that aims to render a system unusable by targeting it with massive amounts of traffic. The literature contains several datasets that could be used to quantify the effectiveness of such attacks. These datasets contain captured network traffic and measure the success of an attack by the amount of traffic it generated. However, the amount of traffic is not the only metric that should be able to measure a DDoS attack. To handle the attack, the victim would be affected in other facets like memory, processing, and others. Furthermore, the traditional DDoS dataset is quite generic and insights gained from them cannot necessarily be applicable to cloud computing.In this paper, we propose a new DDoS dataset that looks at the actual impact on a victim that resides in the Cloud. We observed more than 230 performance indicators that measure how the key victim’s resources, RAM, CPU, network, and disk are affected during the attacks. We methodically captured the dataset and have broken them down into different scenarios that could help us better study DDoS attacks in the Cloud and DDoS attacks in general. The features of our dataset and grouped into seven categories which could help us further comprehend the granularity of these attacks.