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04/22/2001 ecs289K: Intention Driven iTrace 1 ecs298k Intention-Driven iTrace lecture #6 Dr. S. Felix Wu Computer Science Department University of California, Davis http://www.cs.ucdavis.edu/~wu/ [email protected]

04/22/2001ecs289K: Intention Driven iTrace1 ecs298k Intention-Driven iTrace lecture #6 Dr. S. Felix Wu Computer Science Department University of California,

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Page 1: 04/22/2001ecs289K: Intention Driven iTrace1 ecs298k Intention-Driven iTrace lecture #6 Dr. S. Felix Wu Computer Science Department University of California,

04/22/2001 ecs289K: Intention Driven iTrace 1

ecs298kIntention-Driven iTracelecture #6

Dr. S. Felix Wu

Computer Science Department

University of California, Davishttp://www.cs.ucdavis.edu/~wu/

[email protected]

Page 2: 04/22/2001ecs289K: Intention Driven iTrace1 ecs298k Intention-Driven iTrace lecture #6 Dr. S. Felix Wu Computer Science Department University of California,

04/22/2001 ecs289K: Intention Driven iTrace 2

A Statistic Problem with iTrace

• Routers closer to the victims have higher probability to generate iTrace packets toward the true victims.

• Routers closer to the DDoS slaves might have relatively small probability (smaller than the routers around the victims) to generate “useful” iTrace packets.

Page 3: 04/22/2001ecs289K: Intention Driven iTrace1 ecs298k Intention-Driven iTrace lecture #6 Dr. S. Felix Wu Computer Science Department University of California,

04/22/2001 ecs289K: Intention Driven iTrace 3

“Usefulness”• Let’s think??

24 16 0 112 12425 125

Page 4: 04/22/2001ecs289K: Intention Driven iTrace1 ecs298k Intention-Driven iTrace lecture #6 Dr. S. Felix Wu Computer Science Department University of California,

04/22/2001 ecs289K: Intention Driven iTrace 4

Two answers

• It carries attack packets.

• It carries attack packets from a router that is very close to the original slaves

Page 5: 04/22/2001ecs289K: Intention Driven iTrace1 ecs298k Intention-Driven iTrace lecture #6 Dr. S. Felix Wu Computer Science Department University of California,

04/22/2001 ecs289K: Intention Driven iTrace 5

Two measures

• P(U-iTrace)– When an iTrace message is generated, what is

the probability that this iTrace message is “useful” (i.e., it carries an attack packet)?

• P(U-iT-sec)– What is probability for a router to generate at

least ONE “useful” iTrace message in a second?

Page 6: 04/22/2001ecs289K: Intention Driven iTrace1 ecs298k Intention-Driven iTrace lecture #6 Dr. S. Felix Wu Computer Science Department University of California,

04/22/2001 ecs289K: Intention Driven iTrace 6

Example: Multi-S Single-V

Slave R1 R2 Victim

1K attack-pkt/sec 19K normal-pkt/sec P(U-iTrace) = 5% #iTrace/sec = 1 P(U-iT-sec) = 5%

4K attack-pkt/sec196K normal-pkt/sec P(U-iTrace) = 2% #iTrace/sec = 10 P(U-iT-sec) = 18%

200K attack-pkt/sec200K normal-pkt/sec P(U-iTrace) = 50% #iTrace/sec = 20 P(U-iT-sec) = 99.999%

980K attack-pkt/sec 20K normal-pkt/sec P(U-iTrace) = 98% #iTrace/sec = 50 P(U-iT-sec) = 100%

Page 7: 04/22/2001ecs289K: Intention Driven iTrace1 ecs298k Intention-Driven iTrace lecture #6 Dr. S. Felix Wu Computer Science Department University of California,

04/22/2001 ecs289K: Intention Driven iTrace 7

Motivation

• About (K* 0.005%) of our network resources will be spent on iTrace packets.

• Then, we hope we can spend the resources on more “useful” iTrace packets.

Page 8: 04/22/2001ecs289K: Intention Driven iTrace1 ecs298k Intention-Driven iTrace lecture #6 Dr. S. Felix Wu Computer Science Department University of California,

04/22/2001 ecs289K: Intention Driven iTrace 8

Three Types of Nodes

• DDoS victim with the intention to trace the slaves.

• DDoS victim without the intention.

• non-DDoS victims (assuming they do not have the intention as well -- and very likely they hope they won’t receive ones).

Page 9: 04/22/2001ecs289K: Intention Driven iTrace1 ecs298k Intention-Driven iTrace lecture #6 Dr. S. Felix Wu Computer Science Department University of California,

04/22/2001 ecs289K: Intention Driven iTrace 9

Intention-driven iTrace

• Different destination hosts, networks, domains/ASs have different “intention levels” in receiving iTrace packets.– We propose to add one “iTrace-intention” bit.

• Some of them might not care about iTrace, and some of them might not be under DDoS attacks, for example.

Page 10: 04/22/2001ecs289K: Intention Driven iTrace1 ecs298k Intention-Driven iTrace lecture #6 Dr. S. Felix Wu Computer Science Department University of California,

04/22/2001 ecs289K: Intention Driven iTrace 10

a little mathematics...

S2V: 2%S2B:48%S2C:25%S2D:25%

I: 1I: 0I: 0I: 1

Intention forreceiving iTrace.

V’s probability to receive iTrace packets: 7.41%0.02 / (0.02 + 0 + 0 + 0.25) = 0.0741

PiTrace(V) = (Ptraffic(V) * I(V)) / (Ptraffic(n) * I(n)) dst

Page 11: 04/22/2001ecs289K: Intention Driven iTrace1 ecs298k Intention-Driven iTrace lecture #6 Dr. S. Felix Wu Computer Science Department University of California,

04/22/2001 ecs289K: Intention Driven iTrace 11

Example: Multi-S Two-V

Slave R1 R2 Victim

4K att-v1-pkt/sec 50K att-v2-pkt/sec146K normal-pkt/sec

P(U-iTrace) = 2% #iTrace/sec = 10 P(U-iT-sec) = 18%

I(Victim-1) = 1 P(U-iTrace) = 7.4% P(U-iT-sec) = 53.7%

P(U-iTrace) = 25% #iTrace/sec = 10 P(U-iT-sec) = 95%

I(Victim-2) = 1 P(U-iTrace) = 92.6% P(U-iT-sec) = 100.0%

Page 12: 04/22/2001ecs289K: Intention Driven iTrace1 ecs298k Intention-Driven iTrace lecture #6 Dr. S. Felix Wu Computer Science Department University of California,

04/22/2001 ecs289K: Intention Driven iTrace 12

Page 13: 04/22/2001ecs289K: Intention Driven iTrace1 ecs298k Intention-Driven iTrace lecture #6 Dr. S. Felix Wu Computer Science Department University of California,

04/22/2001 ecs289K: Intention Driven iTrace 13

0

16

48

32

20

36

28

24

40

44

52

56

60

232221

252627

293031

414243

535455

636261

373839

474645

595857

64

80

96

112

84

88

92858687

89 90 91

939495

100

104

108

116

120

124

101 102 103

105

106

107

109 110 111

117

118

119

121 122 123

125

126

127

Test-bed topology

133

49

17

6581

97113

Page 14: 04/22/2001ecs289K: Intention Driven iTrace1 ecs298k Intention-Driven iTrace lecture #6 Dr. S. Felix Wu Computer Science Department University of California,

04/22/2001 ecs289K: Intention Driven iTrace 14

node24 (single attack)

4 21 40 78 123 108265

943

1515 1653 16181813

0

500

1000

1500

2000

1k 10k 20k 30k 40k 50k

attack-rate (packets/sec)

Th

e #

of

us

efu

l iT

race

m

es

sag

es

Original iTrace

Intention-Driven iTrace

Node16 (single attack)

2 26 51 77 114 14469

1406

2288 2444 2460 2574

0500

10001500200025003000

1k 10k 20k 30k 40k 50k

attack rate (packets/sec)

Th

e #

of

us

efu

l iT

race

m

es

sag

es

Original iTrace

Intention-Driven iTrace

Page 15: 04/22/2001ecs289K: Intention Driven iTrace1 ecs298k Intention-Driven iTrace lecture #6 Dr. S. Felix Wu Computer Science Department University of California,

04/22/2001 ecs289K: Intention Driven iTrace 15

Node0 (single attack)

5 27 45 82 118 129124

2251

31052444 2460 2574

0

1000

2000

3000

4000

1k 10k 20k 30k 40k 50k

attack-rate (packets/sec)

Th

e #

of

us

efu

l iT

race

m

es

sag

es

Original iTrace

Intention-Driven iTrace

node112 (single attack)

4 27 57 71 107 12644

1041

16541873 1949 2089

0

500

1000

1500

2000

2500

1k 10k 20k 30k 40k 50k

attack rate (packets/sec)

Th

e #

of

us

efu

l iT

race

m

es

sag

e

Original iTrace

Intention-Driven iTrace

Page 16: 04/22/2001ecs289K: Intention Driven iTrace1 ecs298k Intention-Driven iTrace lecture #6 Dr. S. Felix Wu Computer Science Department University of California,

04/22/2001 ecs289K: Intention Driven iTrace 16

Node124 (single attack)

3 20 68 92 105 129

1329 1360 1389 1415 1440 1463

0

500

1000

1500

2000

1k 10k 20k 30k 40k 50k

attack rate (packets/sec)

Th

e #

of

us

efu

l iT

race

m

es

sag

es

Original iTrace

Intention-Driven iTrace

Page 17: 04/22/2001ecs289K: Intention Driven iTrace1 ecs298k Intention-Driven iTrace lecture #6 Dr. S. Felix Wu Computer Science Department University of California,

04/22/2001 ecs289K: Intention Driven iTrace 17

Issues• How to determine the intention bit?

– Policy to set the bit.

• How to distribute the intention bits to routers globally?– Utilize/extend BGP!

• How to use the intention bits at each router?

Page 18: 04/22/2001ecs289K: Intention Driven iTrace1 ecs298k Intention-Driven iTrace lecture #6 Dr. S. Felix Wu Computer Science Department University of California,

04/22/2001 ecs289K: Intention Driven iTrace 18

How to distribute I(n)?• YABE: (Yet Another BGP Extension)

– For every BGP route update, we include I(n) as a new string in the community attribute:

• 0x[iTrace-Intention]:0x[0-1] (optional & transitive)

– These I(n) values will be forwarded or even aggregated by the routers who understand this new community attribute.

• aggregation: I(new) = max {I(n)}

– Rate-Limiting on Intention Update:• should not be more frequent than Keep-Alive messages.

• should not trigger any major route computation.

Page 19: 04/22/2001ecs289K: Intention Driven iTrace1 ecs298k Intention-Driven iTrace lecture #6 Dr. S. Felix Wu Computer Science Department University of California,

04/22/2001 ecs289K: Intention Driven iTrace 19

The iTrace Statistics Model

Packetbuffering

Routingtable

lookup

Forwardprocess

iTraceStochastic

Process

Should this packet be iTraced?

Yes, we shouldgenerate an iTracefor this packet?

Page 20: 04/22/2001ecs289K: Intention Driven iTrace1 ecs298k Intention-Driven iTrace lecture #6 Dr. S. Felix Wu Computer Science Department University of California,

04/22/2001 ecs289K: Intention Driven iTrace 20

iTrace Trigger

Packetbuffering

Routingtable

lookup

Forwardprocess

iTraceStochastic

Process

If yes, pick the Nth packetin the buffer….

Should we generatean iTrace messagenow?

iTraceTrigger

Page 21: 04/22/2001ecs289K: Intention Driven iTrace1 ecs298k Intention-Driven iTrace lecture #6 Dr. S. Felix Wu Computer Science Department University of California,

04/22/2001 ecs289K: Intention Driven iTrace 21

A simple design

BGP table I(n) iTrace bit

iTraceProcess

Add two bits to the routing table:(1). I(n): Intention Bit Value associated with this entry

(2). iTrace bit: whether we need to generate an iTrace message for this entry now.

per ~20K pkts

Page 22: 04/22/2001ecs289K: Intention Driven iTrace1 ecs298k Intention-Driven iTrace lecture #6 Dr. S. Felix Wu Computer Science Department University of California,

04/22/2001 ecs289K: Intention Driven iTrace 22

Handling an iTrace Trigger

BGP table I(n) iTrace bit

iTraceProcess

• If all I(n)’s are zero, shut-off the iTrace trigger process.

• Set the iTrace bit on all the entries with I(n) = 1.

Page 23: 04/22/2001ecs289K: Intention Driven iTrace1 ecs298k Intention-Driven iTrace lecture #6 Dr. S. Felix Wu Computer Science Department University of California,

04/22/2001 ecs289K: Intention Driven iTrace 23

152.1.23.0/24 1 0169.20.3.0/24 0 0192.1.0.0/16 0 0

207.3.4.183/20 1 0152.1.0.0/16 1 0155.0.0.0/16 0 0

152.1.23.0/24 1 1169.20.3.0/24 0 0192.1.0.0/16 0 0

207.3.4.183/20 1 1152.1.0.0/16 1 1155.0.0.0/16 0 0

(1).BeforeiTracetrigger:

(2).AfteriTracetrigger:

I(n) iTrace bit

Page 24: 04/22/2001ecs289K: Intention Driven iTrace1 ecs298k Intention-Driven iTrace lecture #6 Dr. S. Felix Wu Computer Science Department University of California,

04/22/2001 ecs289K: Intention Driven iTrace 24

152.1.23.0/24169.20.3.0/24192.1.0.0/16

207.3.4.183/20152.1.0.0/16155.0.0.0/16

(3).AfteriTracesent:

1 00 00 01 01 00 0

I(n) iTrace bit

Page 25: 04/22/2001ecs289K: Intention Driven iTrace1 ecs298k Intention-Driven iTrace lecture #6 Dr. S. Felix Wu Computer Science Department University of California,

04/22/2001 ecs289K: Intention Driven iTrace 25

Processing Overhead

Processing for each data packet:1. if the iTrace flag bit is 1,

(1). send an iTrace message for this data packet.(2). reset all the iTrace bits to 0.

1/20K iTrace message trigger occurs:1. Set all the iTrace bits on if I(n) = 1.

Page 26: 04/22/2001ecs289K: Intention Driven iTrace1 ecs298k Intention-Driven iTrace lecture #6 Dr. S. Felix Wu Computer Science Department University of California,

04/22/2001 ecs289K: Intention Driven iTrace 26

The Aggregation Problem

Slave R1 R2 Victim

4K att-v1-pkt/sec

50K att-v2-pkt/sec146K normal-pkt/sec

P(U-iTrace) = 2% #iTrace/sec = 10 P(U-iT-sec) = 18%

I(Victim-1) = 1 P(U-iTrace) = 7.4% for 4K traffic. P(U-iT-sec) = 53.7%

4K att-v1-pkt/sec 16K agg-v1-pkt/sec 50K att-v2-pkt/sec130K normal-pkt/sec

P(U-iTrace) = 2% #iTrace/sec = 10 P(U-iT-sec) = 18%

I(Victim-1) = 1 P(U-iTrace) = 5.7% for 20K traffic. P(U-iT-sec) = 44.4%

Page 27: 04/22/2001ecs289K: Intention Driven iTrace1 ecs298k Intention-Driven iTrace lecture #6 Dr. S. Felix Wu Computer Science Department University of California,

04/22/2001 ecs289K: Intention Driven iTrace 27

Summary for Intention iTrace• Improve the probability of “useful” iTrace.• Require some “minor” changes to the router

forwarding process.• Require another BGP extension.

– We need to verify that this extension will be interoperable well with existing BGP nodes.

• The amount of generated iTrace messages should be no more than the current iTrace proposal.