Scanning Computer Viruses with Reduced Virus Definition File s1090009 Daisuke Anzai Supervised by Prof. H Toyoizumi.

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Presentation transcript:

Scanning Computer Viruses with Reduced Virus Definition File s Daisuke Anzai Supervised by Prof. H Toyoizumi

Scanning System Anti-virus software Virus Definition file scan Supply the information matching

Virus Definition File Since 1986, computer viruses have been increased extremely fast Now, there are more than kind of virus information has already published in the virus definition file

Problems This file length effects scan time These viruses will be increasing and more kinds in near future To scan them, server has big loading

Purpose Describe the possibility of reducing virus definition file Using M/D/1 queuing model, evaluate this server’s performance

Condition Virus detected by InterScan VirusWall which installed in the mail server of the University of Aizu are logged at Information Processing Center As a simulation data, use the data in last November

Virus log list

Virus Character The probability that a specific virus come again is high if the virus arrived many in recently The definition file must have efficacy against the new type and new type will appear one after another

Algorithm Logged everyday Sum of log during I.1 month (30 days) II.1 week (7 days) III.1 day Change the rank to descending order and elect top n

Example of algorithm (1 month method, n=10) Sum of log from 10/2 to 10/ … 10. Scan 11/2 Sum of log from 10/3 to 11/ … 10. Scan 11/1 Virus definition file Log file

The Rate of Eliminating Virus Mail (n=10)

The Elimination Rate of Virus Mail (average the 7days)

Queuing Theory To calculate the probability can be received service have not waiting, the average length in queue, and average time from arriving to leaving Queuing classify several kinds by distribution of arrival and service, number of windows, and existence of procession limit

To client Probability waiting will arise when a mail arrived (  ) Number of mails in queue (L) Waiting time (W) Scanning time (S) Arrival rate ( ) Modeling of M/D/1 queuing system start finish

Define and  The rate of average arrival ( ) –Assume that mails arrived on a hour. When 1 second, average of arrival is The rate of average service (  ) –It assume that the time need to scan for one mail is S(second). Then,

Reducing Assume that kinds of virus information published in virus definition file currently, and reducing definition file published only n kinds. New service rate S’ define as

Length in Queue and Waiting Time

The Relation S and W

Result If scanning viruses are several ten kinds, it is low risk for users when the scanning is efficiently Instead of using the waist time to lookup old viruses, server can use the processing ability to scan new type viruses which hard to detect

Future Works Research the measure against the attacking viruses in a special day

Reference (Mathematics) 1.D.P.Heyman, M.J.Sobel, Stochastic Models, Sheldon M.Ross, Stochastic Process Second Edition, 1996

Reference (Virus information) 4.Symantec, 5.Information Processing Center, int/labs/istc/ int/labs/istc/