1 / 12

Epidemic spreading in complex networks with degree correlations

Epidemic spreading in complex networks with degree correlations. Authors: M. Boguna, R. Pastor-Satorras, and A. Vespignani. Publish: Lecture Notes in Physics: Statistical Mechanics of Complex Networks, 2003 Presenter: Cliff C. Zou. Background. Limitation of Internet worm models

britanni-farmer
Download Presentation

Epidemic spreading in complex networks with degree correlations

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Epidemic spreading in complex networks with degree correlations Authors: M. Boguna, R. Pastor-Satorras, and A. Vespignani. Publish: Lecture Notes in Physics: Statistical Mechanics of Complex Networks, 2003 Presenter: Cliff C. Zou

  2. Background • Limitation of Internet worm models • Extended from simple epidemic model • Homogeneous assumption • No topology considered • Suitable for scan-based worms • Not suitable for modeling topological malware • Email viruses • P2P malware

  3. Objective • Provide epidemic analytical models for topological networks • Cover both correlated networks and uncorrelated networks • We only consider uncorrelated networks here

  4. Model Notations • : infection prob. via an edge per unit time • P(k): fraction of nodes with degree k • Only consider SI model • ik(t): fraction of infected in k-degree hosts • hki = k k P(k): average degree

  5. Topological Model I • (t): prob. that any given link points to an infected host • Think each edge has two “end points” • P(k)ik(t)¢ N: # of k-degree infected • P(k)k¢ N: # of end points owned by k-degree nodes

  6. Topological Model II • A newly infected at most has k-1 links to infect others • It is infected through an edge • The edge is useless in infection later

  7. Problems of Models • Implicit assumptions: Homogenous mixing • Assume infected are uniformly distributed • Fact: epidemic spread via topology • Infected are connected (clustered) • Many infectious edges are wasted • Results: • Models overestimate epidemic spreading speed

  8. A Illustration • 16 infectious “end points” • Only 10 effective infection links • Model I: 16, overestimate 60% • Model II: 12, overestimate 20%

  9. Simulation Results Power law network Random network

  10. How to Improve Model? • Remove wasted edges in modeling • Virtual removal hosts • Hosts with few/no links to vulnerable hosts • How to proceed? • I don’t know yet

  11. Security Research Major Conferences • Tier-1: • IEEE Symposium on Security and Privacy (IEEE S&P) • ACM Computer Communication Security (CCS) • Usenix Security Symposium • Annual International Cryptology Conference (CRYPTO) • Tier-2: • NDSS: Network and Distributed System Security • ACSAC: Annual Computer Security Applications Conference • DSN: dependable system and network • ESORICS: European Symposium on Research in Computer Security • RAID: Recent Advances in Intrusion Detection

  12. Technical News • ACM techology news: • http://www.acm.org/technews/articles/2006-8/0130m.html • Information Security Magazine: • http://informationsecurity.techtarget.com/

More Related