Welcome to EyeAdmin.com

LLC “Laboratory of Network Technologies” (LNT) is an innovative SME, founded in 2004 as a spin-off from the Network Research Laboratory at Moscow Engineering Physics Institute under the financial support program for innovations offered by the Foundation for Assistance to Small Innovative Enterprises. LNT carries out research and development projects in the field of ICT and commercialise the obtained results. LNT has broad experience and capabilities, proved by successfully accomplished projects, in such fields as Computer networks, Information security, Network security, Software engineering, System analysis, Internet technologies, Artificial intelligence, System testing and Quality control. LNT owns authors’ certificates on software for anomaly detection in computer networks, registered at Federal Intellectual Property Service (ROSPATENT). LNT collaborates with the Foundation for Assistance to Small Innovative Enterprises, National Research Nuclear University “MEPHI”, Science & Technology International Park "Technopark in Moskvorechje".

02.09.2010. "VZI" ("Information Security Questions") scientific journal publication

E-mail Print PDF

The third issue of "VZI (Information Security Questions" journal in 2010 has been released. An article about Laboratory's scientific researches was included in this number under the heading "Detection and selfsimilarity evaluation of network traffic stationary fragments".

Article is concerned with the problem of IT infrastructure network traffic analysis with the purpose of network objects behavior regular patterns detection. One of the most relevant tasks is development of complex network behavior analysis methods which do not have strict restrictions of applicability. An approach is proposed for extraction of traffic characteristics value ranges and its' combinations allowing sectioning of overall network traffic into persistent fragments, grouping of fragments achieved and stationarity pre-estimation of resulting streams. For the evaluation of stationarity grade the Hurst exponent was used, which was calculated by several different methods, and all the results was compared. Experiments taken over the real traffic of university network have proven the workability of such approach. Self-similarity degree appeared to be high for a number of network traffic characteristics combinations. Among the advantages of the approach: demonstrativeness increase of behavior profiles model, the ability to localize anomalies in early stages of traffic analysis, improvement of accuracy and productivity of network objects behavior analysis.

The full article text can be found in "eLibrary" web scientific library

 обложка номера

Last Updated on Wednesday, 27 October 2010 13:45  
  

Search in site