Internet Cache Pollution Attacks and Countermeasures Yan Gao, Leiwen Deng, Aleksandar Kuzmanovic, and Yan Chen Electrical Engineering and Computer Science

Internet Cache Pollution Attacks and Countermeasures

Yan Gao, Leiwen Deng, Aleksandar Kuzmanovic, and Yan Chen

Electrical Engineering and Computer Science Department

Northwestern University

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Outline

• Motivation• Pollution Attacks• Evaluation of Pollution Effects• Counter-Pollution Techniques &

Evaluation• Conclusion

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Motivation• Caching has been widely applied in the

Internet– Decrease the amount of requests in server side– Reduce the amount of traffic in the network– Improve the client-perceived latency

• Open proxy caches are used for various abuse-related activities

• Proxy caches themselves become victims– Little attention given to such attacks– Existing pollution attacks mostly on content

pollutions on P2P systems

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Contributions• Propose a class of pollution attacks targeted

against Internet proxy caches– Locality-disruption (LD) attacks – False-locality (FL) attacks

• Analyze the resilience of the current cache replacement algorithms to pollution attacks

• Propose two cache pollution detection mechanisms– Detect LD, FL attacks, and their combination– Leverage data streaming computation techniques

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Outline



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Pollution Attack Scenarios (I)

Campus networkInternet

CacheCache

ISP1 ISP2

Downloaded traffic

Content Server

C lient

Requests

Attacking a web cache Attacking an ISP cache

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Pollution Attack Scenarios (II)

L o ca l D N S S erv er

R o o t D N S S erv er

T L D D N S S erv er

A u th o rita tiv eD N S S erv er

P o llu tio n A tta ck

E n d U ser

......

①

② ③ ④

⑤

⑥

⑦

⑧

Pollution attack against a local DNS server

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Pollution Attack: Locality Disruption

…...

. …...

.

Cache

…...

. …...

.

Cache

Before attack After attack

Popular filesNew

unpopular files

• Goal: degrade cache efficiency by ruining its file locality

• Activities: continuously generate requests for new unpopular files

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Pollution Attack: False Locality

…...

. …...

.

Cache

…...

. …...

.

Cache

Before attack After attack

Popular filesBogus

popular files

• Goal: degrade the hit ratio by creating false file locality

• Activities: repeatedly request the same set of unpopular files

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Outline



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Evaluation Methodology

• Discrete-event simulator – Multiple DoS behaviors– Multiple workload characterizing behaviors– Effects of access and local network capacities

• Workloads– P2P [K. Gummadi et al. ACM SOSP 03]– Web [F. Smith et al. SIGMETRICS 01]– NAT effects

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Cache Replacement Algorithms

• Least Recently Used (LRU) algorithm – Evict the least recently accessed document first

• Least Frequently Used (LFU) algorithm – Evict the least frequently accessed document first

• Greedy Dual-Sized Frequency (GDSF) algorithm– Consider the frequency of the documents– Allow smaller document to be cached first– Use dynamic aging policy

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Baseline Experiments• Locality-disruption attacks

Small percent of malicious requests can significantly degrade the overall hit ratio

Total hit ratio = requests_total#

requests_hit#

Including attackers’ requests and regular users’ requests

Stealthy! (4%)

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Baseline Experiments• False-locality attacks

Total hit ratio is not a good indicator for attacks

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BHR(n)BHR(a)BHR(n)

BHR(n)—byte hit ratio of regular clients without attacks

BHR(a)—byte hit ratio of regular clients with attacks

Byte damage ratio =

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Replacement Algorithms • Locality-disruption attacks

LRU and LFU are more resilient to attacks, but still can not protect cache from pollution

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Outline



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Detecting Locality Disruption Attacks

• Observations:

– Low total hit ratio

– Short average life-time of all cached files

• Design:

– Detection: compute the average durations for all files in the cache

– Mitigation: recognize the attackers

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Detecting False Locality Attacks• Observations:

– Clients who request a similar set of files residing in the cache

– The repeated requests from the same IP to cached files

• Design:– Large number of repeated requests– Large percent of repeated requests

• Scalability:– Attacker-based detection: Bloom filter– Object-based detection: Probabilistic Counting with

Stochastic Averaging (PCSA)

cachetheinhitsrequeststotalrequestsrepeated

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Evaluation of Pollution Detection• Results for false-locality attacks, more in paper

For attacker’s file detection:

True positive ratio =

filessker'attactotal#methodourbyecteddetfilesker'attac#

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• Realize the counter-pollution mechanisms

• Code and more details

http://networks.cs.northwestern.edu/AE/

Implementation

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Conclusions

• Propose and evaluate two classes of attacks: locality-disruption and false-locality attacks

• Show that pollution attacks are stealthy, but powerful, and different replacement algorithms have different resiliency

• Propose and evaluate a set of scalable and effective counter-pollution mechanisms

Documents

Internet Cache Pollution Attacks and Countermeasures Yan Gao, Leiwen Deng, Aleksandar Kuzmanovic, and Yan Chen Electrical Engineering and Computer Science