Quantifying Network Denial of Service: A Location Service Case Study Yan Chen, Adam Bargteil, David Bindel, Randy H. Katz and John Kubiatowicz Computer

Quantifying Network Denial of Service: A Location Service Case

Study

Yan Chen, Adam Bargteil, David Bindel, Randy H. Katz and John Kubiatowicz

Computer Science DivisionUniversity of California at Berkeley

USA

Outline

• Motivation• Network DoS Attacks Benchmarking• Object Location Services• Simulation Setup and Results• Conclusions

Motivations

• Network DoS attacks increasing in frequency, severity and sophistication– 32% respondents detected DoS attacks (1999 CSI/FBI survey)

– Yahoo, Amazon, eBay and MicroSoft DDoS attacked

– About 4,000 attacks per week in 2000

• Security metrics in urgent need– Mission-critical applications built on products claiming various and

suspect DoS resilient properties

– No good benchmark for measuring security assurance

• Desired: A general methodology for quantifying arbitrary system/service resilience to network DoS attacks

Outline

• Motivation• Network DoS Attacks Benchmarking • Object Location Services• Simulation Setup and Results• Conclusions

Network DoS Benchmarking

• QoS metrics– DoS attacks resource availability, a spectrum metric rather

than binary

– General vs. App-specific metrics• General: end-to-end latency, throughput and time to recover

• Multi-dimensional Resilience Quantification– Dimension rank based on importance, frequency, severity &

sophistication

– Application/system specific, hard to generalize

– Solution: be specific in the threat model definition and only quantify resilience in the model

Network DoS Benchmarking (Cont’d)

• Simulation vs. Experiment – Standard & realistic simulation environment specification

• Network configuration

• Workload generation

• Threat model – taxonomy from CERTY Consumption of network connectivity and/or bandwidth

Y Consumption of other resources, e.g. queue, CPU

Y Destruction or alternation of configuration information

N Physical destruction or alternation of network components

Two General Classes of Attacks• Flooding Attacks

– Point-to-point attacks:

TCP/UDP/ICMP flooding,

Smurf attacks

– Distributed attacks:

hierarchical structures

• Corruption Attacks– Application specific

– Impossible to test all, choose typical examples for benchmarking

Outline


Object Location Services (OLS)• Centralized directory services (CDS) vulnerable to DoS

attack– SLP, LDAP

• Replicated directory services (RDS) suffer consistency overhead, still limited number of targets

• Distributed directory services (DDS)– Combined routing & location on overlay network

– Guaranteed success and locality

– Failure and attack isolation

– Examples: Tapestry, CAN, Chord, Pastry

Tapestry Routing and Location• Namespace (nodes and objects)

– Each object has its own hierarchy rooted at Root

f (ObjectID) = RootID, via a dynamic mapping function

• Suffix Routing from A to B– At hth hop, arrive at nearest node hop(h) s.t.

hop(h) shares suffix with B of length h digits

– Example: 5324 routes to 0629 via5324 2349 1429 7629 0629

• Object Location– Root responsible for storing object’s location

– Publish / search both route incrementally to root

• Http://www.cs.berkeley.edu/~ravenben/tapestry

4

2

3

3

3

2

2

1

2

4

1

2

3

3

1

34

1

1

4 3

2

4

Tapestry MeshIncremental suffix-based routing

NodeID0x43FE

NodeID0x13FENodeID

0xABFE

NodeID0x1290

NodeID0x239E

NodeID0x73FE

NodeID0x423E

NodeID0x79FE

NodeID0x23FE

NodeID0x73FF

NodeID0x555E

NodeID0x035E

NodeID0x44FE

NodeID0x9990

NodeID0xF990

NodeID0x993E

NodeID0x04FE

NodeID0x43FE

Routing in Detail

5712

0880

3210

7510

4510

Neighbor MapFor “5712” (Octal)

Routing Levels1234

xxx1

5712

xxx0

xxx3

xxx4

xxx5

xxx6

xxx7

xx02

5712

xx22

xx32

xx42

xx52

xx62

xx72

x012

x112

x212

x312

x412

x512

x612

5712

0712

1712

2712

3712

4712

5712

6712

7712

5712 0 1 2 3 4 5 6 7

0880 0 1 2 3 4 5 6 7

3210 0 1 2 3 4 5 6 7

4510 0 1 2 3 4 5 6 7

7510 0 1 2 3 4 5 6 7

Example: Octal digits, 212 namespace, 5712 7510

Object LocationRandomization and Locality

Outline


Simulation Setup

Fast Ethernet

Fast EthernetT3

T1T1

• Distributed Information Retrieval System Built on top of ns

• 1000-node Transit-stub Topology from GT-ITM– Extended with common

network bandwidth

• Synthetic Workload – Zipf’s law and hot-cold patterns– 500 objects, 3 replicas each on 3 random nodes– Size randomly chosen as typical web contents: 5KB – 50KB

Directory Servers Simulation

• CDS with random replica (CDSr)• CDS with closest replica (CDSo)• RDS: with 4 random widely-distributed nodes, always

return random replica– With random directory server (RDSr)

– With closest directory server (RDSo)

• DDS: simplified version of Tapestry (DDS)– Tapestry mesh statically built with full topology knowledge

– Using hop count as distance metric

Attacks Simulation• Flooding Attacks

– 200 seconds simulation– Vary the number of agents: 1 – 16– Each inject various constant bit stream to targets: 25KB/s –

500KB/s– Targets:

• CDS, RDS: the directory server(s)• DDS: the root(s) of hot object(s)

• Corruption Attacks– Corrupted application-level routing tables of target nodes:

CDS, RDS, DDS– Forged replica advertisement through node spoofing: DDS

• CDS vs. Tapestry– 1 * 100KB/s, 4 *

25KB/s – 4 *100KB/s– Tapestry shows

resistance to DoS attacks

Results of Flooding Attacks

Average response latency Request throughput

Only the flood traffic amount matter? No!

– Bottleneck bandwidth restrict attackers’ power

– Path sharing of clients and attackers

– Hard to identify and eliminate multiple attackers simultaneously

Dynamics of Flooding Attacks• CDS vs. Tapestry (most severe case: 4 *100KB/s)

– Attacks start at 40th second, ends at 110th second

• Time to Recover– CDS (both policies): 40 seconds

– Tapestry: negligible


Results of Flooding Attacks• RDS vs. Tapestry

– 4 * 100KB/s – 16 * 500KB/s

– Both RDS and Tapestry are far more resilient than CDS

– Performance: RDSo > Tapestry > RDSr

Counter DoS attacks: Decentralization and topology-aware locality


Results of Corruption Attacks• Distance Corruption

– One false edge on directory/root server

– Only CDSo (85%) and Tapestry (2.2%) affected

• App-specific attacks– One Tapestry node spoofing to

be root of every object (black square node in the graph)

– 24% of nodes are affected (enclosed by round-corner rectangles)

Resiliency Ranking• Combine multiple-dimensional resiliency quantification

into a single ranking– For multiple flooding attacks, weights are assigned in

proportion to the amount of flood traffic– Normalized with the corresponding performance without

attacks

Flooding (80%)

Distance corruption (10%)

Node spoofing (10%)

Total score

Rank

CDS, rand obj 0.027 N/A N/A 0.2216 4

CDS, opt obj 0.023 0.85 N/A 0.2034 5

Random RDS 0.17 N/A N/A 0.336 3

Optimal RDS 0.48 N/A N/A 0.584 1

DDS,Tapestry 0.35 0.978 0.76 0.4538 2

Outline

• Motivation• Network DoS Attacks Benchmarking• Object Location Services• Simulation Setup and Results• Conclusions

Conclusions

• First attempt for network DoS benchmarking• Applied to quantify various directory services• Replicated/distributed services more resilient than

centralized ones

• Will expand it for more comprehensive attacks and more dynamic simulation

• Will use it to study other services such as web hosting and content distribution

Queuing Theory Analysis(backup slide)

• Assume M/M/1 queuing• Predict the trends and how to choose simulation

parameters to cover enough spectrum

Average response latency (s)

Legitim

ate throughput

X axis: ratio of attack traffic vs. legitimate traffic

Documents

Quantifying Network Denial of Service: A Location Service Case Study Yan Chen, Adam Bargteil, David Bindel, Randy H. Katz and John Kubiatowicz Computer