Deceiving entropy based DoS detection

TL;DR: The validation techniques used for DDoS research are investigated comprehensively and it is proposed to extend them with the inclusion of new validation technique of analyzing real datasets.

TL;DR: This work proposed a fast and an effective entropy-based DDoS detection, and investigated the trade-off between SAE and CNN classifiers by using accuracy and false-positive results.

TL;DR: In this article , the authors proposed a DDoS attack detection and countermeasure scheme based on discrete wavelet transform (DWT) and auto-encoder neural network for SDN.

TL;DR: This book, edited by four of the leaders of the National Science Foundations Global Environment and Network Innovations (GENI) project, gives the reader a tour of the history, architecture, future, and applications of GENI.

TL;DR: This whitepaper proposes OpenFlow: a way for researchers to run experimental protocols in the networks they use every day, based on an Ethernet switch, with an internal flow-table, and a standardized interface to add and remove flow entries.

TL;DR: This work proposes to use several information-theoretic measures, namely, entropy, conditional entropy, relative conditional entropy; information gain, information gain; and information cost for anomaly detection for protection mechanisms against novel attacks.

TL;DR: The concurrent deployment of these technologies on regional and national R&E backbones will result in a revolutionary new national-scale distributed architecture, bringing to the entire network the shared, deeply programmable environment that the cloud has brought to the datacenter.

TL;DR: Methods to identify DDoS attacks by computing entropy and frequency-sorted distributions of selected packet attributes and how the detectors can be extended to make effective response decisions are presented.