By Per Kreuger, Henrik Abrahamsson (auth.), Giorgio Nunzi, Caterina Scoglio, Xing Li (eds.)
This booklet constitutes the refereed court cases of the ninth IEEE Workshop on IP Operations and administration, IPOM 2009, held in Venice, Italy, on October 29-30, 2009, as a part of the fifth overseas Week on administration of Networks and companies, Manweek 2009.
The 12 revised complete papers provided during this quantity have been conscientiously reviewed and chosen from 35 submissions. The papers are equipped in topical sections on administration of caliber of companies and multimedia, community robustness, administration of digital networks, configuration of community assets and applications
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Additional info for IP Operations and Management: 9th IEEE International Workshop, IPOM 2009, Venice, Italy, October 29-30, 2009. Proceedings
Holanda Filho Table 2. Candidate discriminator variables (P2P) F-Ratio Acum Hit Ratio Third quartile of packet inter-arrival time. (server→client) 249,75 45,85% Maximum idle time, calculated as the maximum time between consecutive packets seen in the direction. (client→server) 223,44 50,28% FFT of packet IAT (arctan of the top-ten frequencies ranked by the magnitude of their contribution). (server→client) 172,84 57,52% Candidate Variable Third quartile of total bytes in IP packet. (client→server) 160,47 88,37% The total number of bytes sent in the initial window.
We also developed the mediator hosting the monitor/control plane functionalities. The mediator has the following capabilities. a) Web console for ACL-based filtering configuration b) Receiving the ACL-based filtering configuration file presenting the XML schema c) Selecting the application module to be monitored, including the SIP module or RTP module, in accordance with the ACL-based filter configuration d) Regarding the SIP module, parsing the SIP packets and making a CDR e) Regarding the RTP module, creating QoS metrics and exporting traffic data.
The monitoring window is comprehensive of both working days and weekend days. 2 million packets. 3 Data Processing and Labeling The labeling process enriches the data trace with information about (i) the type and structure of the malicious traffic, (ii) dependencies between single isolated malicious A Labeled Data Set for Flow-Based Intrusion Detection 43 Fig. 1. From raw data (packets and logs) to the labeled data-set activities. The latter is particularly important for a flow-based data set where by design no further detail on the content of the communication is available to the end user.