Dynamics of Malicious Software in the Internet

Post on 08-Jan-2016

39 views 3 download

Tags:

description

Tatehiro Kaiwa, University of Aizu. E-mail:m5081224@u-aizu.ac.jp. Dynamics of Malicious Software in the Internet. 1. Outline. Random Network and Scale-free Network Observed Arrivals of E-mail Simulation Model of Worm Spread Dynamics Local Network Structure Inference - PowerPoint PPT Presentation

Transcript of Dynamics of Malicious Software in the Internet

Dynamics of Malicious Software

in the InternetTatehiro Kaiwa,

University of Aizu.

E-mail:m5081224@u-aizu.ac.jp

1

Outline●Random Network and Scale-free Network

●Observed Arrivals of E-mail

●Simulation Model of Worm Spread Dynamics

●Local Network Structure Inference

●Mathematical Model of Outbreak

●Hub Defense Strategy

●Conclusion

2

Two Model of Network● Model of Network

– Random Network Degree Distribution: bell curve– Scale-free Network Degree Distribution: power-law

3

Scale-free and Preferential Attachment

Scale-free Network is a network with power-law degree distribution.

4

Structure of E-mail Network

Degree Distribution of an e-mail network.Reference:Holger Ebel, Lutz-Ingo Mielsch, and Stefan Bornholdt,“Scale-free topology of e-mail networks”,Physical Review E 66, 2002

*k: The number of links.

5

Spoofed From-field

● The From-filed of an e-mail message a worm sends is varies and/or is spoofed.

● It is almost impossible to identify where a worm sends the e-mail and how many worms send observed e-mails.

● It is only arrival intervals that we can obtain a correct data from received e-mails.

6

Observed Arrivals of E-mail

● There are log data* of the time on which each e-mail messages with a worm attached arrived at University of Aizu. * http://web-int/labs/istc/ipc/Security/virus/index.html

7

Simulation Model of Worm Spread Dynamics

8

Comparison between Simulation and Observed Data

9

Arrival Intervals of Simulationi) ii)

iii) i) mk:115.619 ii) mk:92.15

iii) mk:61.95

*mk : Mean of Number of links neighbors have.

10

Mathematical Model of Outbreak

][2

][1][

eME

MESE

11

Hub Defense Strategy (1)

*h = Number of immune hub nodes

Difference of Number of immune hub nodes.

12

Hub Defense Strategy (2)

r = Number of immune nodes selected randomly. h= Number of immune hub nodes.

Comparison Between Hub Defense and Random Defense

13

Conclusion● Observing arrival intervals, we can estimate damage

of a worm and estimate a network structure around observer.

● We can confirm that hub defense strategy is an effective method in this network even though the number of immune hub nodes are not much enough.

14

Thank you

15