Statistical approaches to DDoS attack detection and response

By L. Feinstein, D. Schnackenberg, R. Balupari, D. Kindred. Proceedings DARPA Information Survivability Conference and Exposition, 2003

551 citations recorded by the source corpus.

How researchers see this paper

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Lucid: A Practical, Lightweight Deep Learning Solution for DDoS Attack Detection (2020, methodology): “[25] proposed a DDoS detection technique based on the computation of source IP address entropy and Chi-square distribution.”
Eviction of Misbehaving and Faulty Nodes in Vehicular Networks (2007, background): “[35], [36], but mainly in the context of the wired Internet.”
An empirical evaluation of entropy-based traffic anomaly detection (2008, methodology): “Categories and Subject Descriptors C.2.3 [Computer-Communication-Networks]: Network Operations network management, network monitoring General Terms Management, Measurement Keywords Entropy, Anomaly Detection 1.”
A Survey on Machine Learning Techniques for Cyber Security in the Last Decade (2020, methodology): “Statistical models, association rules, ANN, and KNN, were used to detect the anomaly-based detection techniques on a host [418]–[420].”
Mining anomalies using traffic feature distributions (2005, background): “for example for problems in intrusion detection by [26], and to detect DOS attacks [9].”