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The Taming of The Shrew: Mitigating Low-Rate TCP-targeted Attack

Chia-Wei Chang, Seungjoon Lee, Bill Lin, Jia Wang

Shrew Attack [Kuzmanovic03]

TCP-targeted low-rate denial-of-service attack

Exploits TCP’s retransmission timeout• Periodic burst (with period T) synchronized with TCP minRTO

– R: large enough to cause packet drops– L: long enough to induce timeouts

• Victims experience repeated loss of retransmissions• Near-zero throughput

Shrew attack

TCP victim

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Related Work

BGP (Border Gateway Protocol) runs on top of TCP• Shrew attack can cause BGP session close [Zhang07]

• Potentially can disrupt Internet routing

Detection/Mitigation Schemes• Active Queue Management, randomize minRTO

• Insufficient to fully mitigate attack

• Previous schemes to identify attack flows• Periodic pattern monitoring, auto-correlation analysis, wavelet-

based approach, frequency domain spectrum analysis• Prohibitive to realize in high-speed networks

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Outline

SAP (Shrew Attack Protection) Design Overview• Deployment Consideration

Testbed Experiments

Simulation Experiments

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Shrew Attack Protection

Priority-based filtering mechanism• Identifies victims and prioritizes their flows

– Can help external systems identify attack flows

• Router monitors drop rate for each potential “victim”– Low drop rate: Packets are treated normal (i.e., low priority)– High drop rate: Packets are tagged high priority, and

preferentially admitted to output queue

• Protects victims from losing consecutive packets

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SAP Components

Drop Rate Collector• Continuously monitors instantaneous per-aggregate drop rate

– Counters for arrivals and drops for each potential victim– For the current time interval and recent history (e.g., total of 10 time intervals)

Fair Drop Rate Controller

• Pavg: Average drop rate for all monitored aggregates

• Pfair = max(Pavg, Pmin)

– No intervention if drop rate is under a threshold

Differential Tagging & Preferential Drop

• Packets are tagged high-priority if instantaneous drop rate is beyond Pfair

– Relatively short sequence of losses can trigger differential tagging– E.g., Pfair = 5%, and 9 successful transmissions and one drop

• Preferential dropping is implemented in modern routers (e.g., WRED)

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SAP Maintains Statistics for Aggregates

Maintaining per-flow statistics for all flows is typically infeasible

SAP uses application-level aggregates• E.g., destination port

• Maintaining aggregate-level information is feasible in hardware• E.g., 65536 TCP ports• 20 counters * 4 bytes * 60K aggregates ~ 5MB of SRAM

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Discussions

Different flows can be treated as a single aggregate• Attacker may use protected TCP port

• Shrew attack may use protected TCP port• Malicious flow may intentionally cause packet drops and trigger

elevated priority

• SAP still prevents session close and improves victim’s throughput

• SAP can help external systems narrow down attack flows

Different aggregates may vary in the number of flows• SAP preserves per-flow throughput

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Experiment Setup

Simulation Study using FTP, HTTP, BGP flows• ns-2 simulator

• augmented with SAP

Validation using real router testbed• 1 Juniper router, 2 Ethernet switches, 3 PCs

• BGP flow only (using Zebra and real BGP trace)

Simulation Testbed

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Simulation vs. Testbed

T = 1sec, L = 0.3sec, R = 15, 18, 20Mbps

Packet drop rates are highly close

Juniper Testbed ns-2 simulation

Attack rate BGP Attack flow BGP Attack flow

15 Mbps 17.4% 33.1% 18.1% 35.0%

18 Mbps 28.1% 45.2% 28.3% 44.8%

20 Mbps 28.2% 50.3% 29.0% 49.8%

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Simulation: Throughput and Drop Rate

• R = 15Mbps, T = 1sec, L = 0.3sec

• RED is not enough to mitigate Shrew attack

• BGP session is closed

Throughput (in Kbps) Drop Rate (in %)

FTP HTTP BGP Attack FTP HTTP BGP Attack

No-attack 4996 4995 4.5 - 0.2 0.2 5.8 -

RED Attack ~0 ~0 ~0 3462 ~100 ~100 ~100 22.7

SAP

Un-protected

Port

ProtectedPort

HTTP

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Simulation: Throughput and Drop Rate

• SAP protects legitimate TCP flows from losing multiple packets

• Thus, enables high throughput in the presence of attack

Throughput (in Kbps) Drop Rate (in %)

FTP HTTP BGP Attack FTP HTTP BGP Attack

No-attack 4996 4995 4.5 - 0.2 0.2 5.8 -

RED Attack ~0 ~0 ~0 3462 ~100 ~100 ~100 22.7

SAP

Un-protected

Port3975 3870 5.4 1784 3.0 3.0 6.1 57.0

ProtectedPort

HTTP

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Simulation: Throughput and Drop Rate

• Shrew attack using protected port is more effective against SAP– Pavg becomes higher due to attack flow

• Still, SAP keeps all TCP sessions alive– SAP prevents consecutive packet drops

Throughput (in Kbps) Drop Rate (in %)

FTP HTTP BGP Attack FTP HTTP BGP Attack

No-attack 4996 4995 4.5 - 0.2 0.2 5.8 -

RED Attack ~0 ~0 ~0 3462 ~100 ~100 ~100 22.7

SAP

Un-protected

Port3975 3870 5.4 1784 3.0 3.0 6.1 57.0

ProtectedPort 83 76 1.8 3410 8.9 9.1 22 23

HTTP

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Simulation: Throughput and Drop Rate

• HTTP flows get higher throughput when Shrew attack uses HTTP

• SAP keeps all sessions alive

Throughput (in Kbps) Drop Rate (in %)

FTP HTTP BGP Attack FTP HTTP BGP Attack

No-attack 4996 4995 4.5 - 0.2 0.2 5.8 -

RED Attack ~0 ~0 ~0 3462 ~100 ~100 ~100 22.7

SAP

Un-protected

Port3975 3870 5.4 1784 3.0 3.0 6.1 57.0

ProtectedPort 83 76 1.8 3410 8.9 9.1 22 23

HTTP 75 1760 1.7 3281 9.0 1.1 22 28

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Conclusions

SAP (Shrew Attack Protection)• Simple counter-based filtering mechanism

– Priority-tagging and preferential drop

• Uses application-level aggregates, not per-flow statistics– Implementable using today’s hardware

• Identifies and protects victims– Can help identify attack flows

• Mitigates Shrew attack in various attack scenarios

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