A model to determine the impact of DDoS attacks using Twitter data

Distributed denial of service (DDoS) attacks, which are designed to prevent legitimate users from accessing specific network systems, have become increasingly common over the past decade or so. These attacks make services such as Facebook, Reddit and online banking sites extremely slow or impossible to use by exhausting network or server resources (e.g., bandwidth, CPU and memory). Researchers worldwide have been trying to develop techniques to prevent DDoS attacks or rapidly intervene in order to reduce their negative effects. An important step in counteracting such attacks is the prompt collection of feedback from users to determine their impact and come up with targeted solutions. With this in mind, a team of researchers at the University of Maryland have developed a machine-learning model that could help to determine the scale of impact of DoS attacks as they are happening based on tweets posted by users.