DoS Attack Detection and Mitigation

TL;DR: An integrated feature selection (IFS) method which consists of three stages and integration of two different methods to select features which highly contribute to the detection of various types of DDoS attacks, and shows that the performance of the model improves if feature space is reduced by 77%.

TL;DR: An unsupervised machine learning (ML)‐based approach for the detection of different types of DDoS attacks is devised by augmenting the performance of K‐means clustering algorithm with the aid of a hybrid method for feature selection and extraction.

TL;DR: Experiments presented in this dissertation shows that a DoS attack that exploits the side-channel vulnerability against the synchrophasor network can lead to the power system in stability, andSide-channel analysis extracts information by observing implementation artifacts without knowing the actual meaning of the information.

TL;DR: A compromise approach is presented that creates software-defined data exchanges (SDX), which mix traffic randomization with VPN concepts, and contrast this approach with the Tor overlay network and provide some performance data.