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Secure Grid Computing - GridSec Project - University of Southern ...

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NPC-2004 Oct. 18, 2004<br />

Trust Integration over a VPN Ring<br />

Aggregate costs in Single Sign-on<br />

operation over VPN vs. PKI Services<br />

V<br />

V<br />

Site S 2<br />

Site S 1<br />

Site S 4<br />

V<br />

SeGO<br />

Server<br />

VPN<br />

Gateway<br />

Hosts<br />

Site S 3<br />

Physical backbone<br />

VPN tunnel ring<br />

V Trust Vector<br />

Trust vector<br />

propagation<br />

User application<br />

and SeGO server<br />

negotiation<br />

Cooperating gateways working together<br />

to establish VPN tunnels for trust integration<br />

V<br />

Aggregate Service Cost<br />

10<br />

8<br />

6<br />

4<br />

GSI based<br />

VPN based<br />

2<br />

0<br />

10 20 30 40 50 60 70 80 90 100<br />

<strong>Grid</strong> Size<br />

October 18, 2004, Kai Hwang http://<strong>Grid</strong>Sec.usc.edu<br />

7<br />

October 18, 2004, Kai Hwang http://<strong>Grid</strong>Sec.usc.edu<br />

8<br />

Global <strong>Grid</strong>Sec Testing Environment<br />

International Collaborators in USA,<br />

France, China, and Australia<br />

USC NetShield Intrusion Defense System<br />

for Protecting Local Network<br />

<strong>of</strong> <strong>Grid</strong> <strong>Computing</strong> Resources<br />

INRIA,<br />

Sophia<br />

Antipolis,<br />

France<br />

ICT <strong>of</strong> CAS<br />

Beijing,<br />

China<br />

Melbourne<br />

<strong>University</strong>,<br />

Australia<br />

The <strong>Grid</strong>Sec<br />

over Internet<br />

USC Gateway,<br />

Los Angeles<br />

Trojan<br />

Cluster in<br />

IGC Lab.<br />

USC/ISD<br />

Supercluster<br />

USC NetShield Defense<br />

System and Testing<br />

Facilities<br />

Security<br />

Policy<br />

Manager<br />

Security<br />

Database<br />

ISP<br />

The<br />

Internet<br />

Network<br />

Router<br />

The<br />

NetShield<br />

System<br />

Firewall<br />

Datamining for Anomaly<br />

Intrusion Detection (IDS)<br />

Risk<br />

Assessment<br />

System (RAS)<br />

Intrusion<br />

Response<br />

System (IRS)<br />

Victim’s<br />

Internal<br />

Network<br />

October 18, 2004, Kai Hwang http://<strong>Grid</strong>Sec.usc.edu<br />

9<br />

October 18, 2004, Kai Hwang http://<strong>Grid</strong>Sec.usc.edu<br />

10<br />

Security-Driven Heuristics for Trusted Job<br />

Scheduling on Risky <strong>Grid</strong>s<br />

• Min-min heuristics:<br />

‣ For each job, the resource site that gives the earliest completion time is<br />

selected first. The job that has the minimum earliest completion time is<br />

assigned to the selected resource site.<br />

• Sufferage heuristics:<br />

‣ The Sufferage heuristic is based on the idea that better mappings are<br />

generated by assigning a site to a job that would “suffer” most in terms <strong>of</strong><br />

expected completion time<br />

• Three secure and risky scheduling modes:<br />

‣ <strong>Secure</strong> mode – Allocate jobs only to those <strong>Grid</strong> sites with security level<br />

exceeding the job requirement (SD < SL)<br />

‣ Risky mode – Allocate jobs to any available <strong>Grid</strong> sites without checking<br />

the risk level or the job demand<br />

‣ f-risky mode – Allocate jobs to those <strong>Grid</strong> sites taking at most f % risk<br />

Risk scale:<br />

0 f 100%<br />

( ) 0 P fail = P( fail)<br />

f<br />

P( fail )

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