Traffic Data Repository at the WIDE Project

By Kenjiro Cho, Koushirou Mitsuya, A. Kato. USENIX ATC, FREENIX Track, 2000

336 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a2f7d7f3243d9bf082cb9d1668a28fbc2764c144

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Citations in the corpus

MAWILab: combining diverse anomaly detectors for automated anomaly labeling and performance benchmarking (2010, background): “The goal is to find and label anomalies in the traffic from the MAWI archive [9], and to make it available to researchers so that they can refer to it while evaluating their own anomaly”
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges (2017, methodology): “The presented scheme combines subspace clustering and correlation analysis to detect anomalies and provide protection against unknown anomalies; this experiment used WIDE backbone networks data [208] spanning over six years and produced better results then previous K-means based techniques.”
Prefix-preserving IP address anonymization: measurement-based security evaluation and a new cryptography-based scheme (2002, methodology): “The security implications of prefix- preserving anonymization of traffic traces using TCPdpriv [4] are briefly studied in [5] and [18].”
The Grid Workloads Archive (2008, unknown): “The Internet community has since created several other archives, i.e., WIDE, CAIDA's archives, CRAWDAD, and NTTT.”
A nonstationary Poisson view of Internet traffic (2004, unknown): “We use three types of traces for our study: a) Internet backbone traces from an OC48 link, b) traces from the WIDE backbone maintained by the MAWI Working Group Traffic Archive and the WIDE project [2] [9], and c) the “well-known” BCpAug89 and LBL-PKT-4 traces [1] which were analyzed in [28] and [36”
An empirical comparison of botnet detection methods (2014, methodology): “Cho et al. (2000) √ pcap √ - - - Table 1: Summary of available datasets the network and/or individual users behaviors and labels deviations from normal behaviors as anomalous.”
UbeHealth: A Personalized Ubiquitous Cloud and Edge-Enabled Networked Healthcare System for Smart Cities (2018, methodology): “The WIDE-18 trace is provided by MAWI working group [133], which is a part of the WIDE project [134].”