CAIDAPassiveDataUse < Sandbox

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need:  summary of duration, number of samples, volume of compressed and uncompressed traces, i gues the IMPACT-like nubmers, were we putting these datasets in impact.  for all anonymized passive traces.




How about network neutrality measurements? | https://dl.acm.org/citation.cfm?doid=3055535.3040966

Traffic control/management | https://dl.acm.org/citation.cfm?doid=3078505.3078557

Social network behavior inference | https://ajtde.telsoc.org/index.php/ajtde/article/view/106

Another in energy efficiency | https://ieeexplore.ieee.org/document/7917269

Several used for analysis of SDN and floods but not sure which are highlights?

DDoS example? https://ieeexplore.ieee.org/document/7888484

More energy efficient ethernet | https://ieeexplore.ieee.org/document/7728004

Big data, scalable traffic analysis | https://ieeexplore.ieee.org/document/7840901/references#references

Energy efficient routers | https://ieeexplore.ieee.org/document/7542558

Characterization of Evolving Networks for Cybersecurity | https://link.springer.com/chapter/10.1007%2F978-3-319-44257-0_5#Bib1

Bot detection and identification | https://link.springer.com/article/10.1007%2Fs11416-015-0250-2#Bib1

The topic of elephant flows and bloom filters seems to come up often | https://www.sciencedirect.com/science/article/pii/S0140366414003685?via%3Dihub

Network troubleshooting | https://www.usenix.org/conference/nsdi14/technical-sessions/presentation/handigol

Understanding stochastic properties of traffic:  https://link.springer.com/chapter/10.1007/978-3-319-66562-7_4
analyze the self-similarity properties of process using Hurst index, obtained through the wavelet transform method, was employed.
Finally, decimation used a useful auxiliary technique in the process of scale invariance analysis is presented.

Example from ILENS http://www.caida.org/funding/ilens-np/ilens-np_proposal.xml#tth_sEc3.2

3.2  New research opportunities enabled by current data products
Our web site lists publications known to us by non-CAIDA authors that make use of CAIDA data (summarized in Figure 5) [60], a lower bound since we cannot enforce the reporting requirement of our AUP. Researchers have requested CAIDA's topology data to support research in the areas of: modeling IPv4 and IPv6 AS-level topology and routing behavior; alias resolution, router-level, and PoP-level topology discovery (including classified work to support DARPA's Plan X project); topology inference and fault diagnosis; infrastructure failure assessments; machine-learning-based AS classification; incongruity between data plane and control plane paths; improving anycast implementations; new metrics for describing scale-free networks; peer-to-peer system scalability; improving visualization of complex systems; geolocation; modeling of delay; improved traceback for network attacks; and new protocols (extensions of IP) to support attribution and prioritization. Publications reported back to us have covered a variety of topics related to the security and stability of the Internet as critical infrastructure [61,62,63,64]: growth analysis of ISPs [65]; infrastructure improvements in the developing world [66]; interdomain traffic estimation [67]; Internet mapping [68], router-level topology discovery [14,69,70]; tomography [71] and path prediction techniques [72]; evolution of interconnection policies and controversies [73]; risks of Internet partitioning [74]; prefix hijacking [75,76,77]; DDoS attack countermeasures [78,79]; complex network robustness in the face of epidemics [80]; geometric analysis of the Internet topology [81]; complex network theory [82,83]; future Internet architectures; CDN architectures [84]; and a geographic database ("Atlas") of the Internet at the physical layer [85,86].
Topic revision: r3 - 2018-12-22 - KcClaffy