Internet Cache Pollution Attacks and Countermeasures Yan Gao

Internet Cache Pollution Attacks and Countermeasures Yan Gao, Leiwen Deng, Aleksandar Kuzmanovic, and Yan Chen Electrical Engineering and Computer Science Department Northwestern University 1

Outline • • Motivation Pollution Attacks Evaluation of Pollution Effects Counter-Pollution Techniques & Evaluation • Conclusion 2

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 P 2 P systems 3

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 4

Outline • • Motivation Pollution Attacks Evaluation of Pollution Effects Counter-Pollution Techniques & Evaluation • Conclusion 5

Pollution Attack Scenarios (I) Attacking a web cache Attacking an ISP cache 6

Pollution Attack Scenarios (II) ③ ② ④ ⑤ ⑥ ⑦ ① ⑧ Pollution attack against a local DNS server 7

Pollution Attack: Locality Disruption Before attack After attack Popular files …. . . . Cache New unpopular files Cache • Goal: degrade cache efficiency by ruining its file locality • Activities: continuously generate requests for new unpopular files 8

Pollution Attack: False Locality Before attack After attack Popular files …. . . . Cache Bogus popular files Cache • Goal: degrade the hit ratio by creating false file locality • Activities: repeatedly request the same set of unpopular files 9

Outline • • Motivation Pollution Attacks Evaluation of Pollution Effects Counter-Pollution Techniques & Evaluation • Conclusion 10

Evaluation Methodology • Discrete-event simulator – Multiple Do. S behaviors – Multiple workload characterizing behaviors – Effects of access and local network capacities • Workloads – P 2 P [K. Gummadi et al. ACM SOSP 03] – Web [F. Smith et al. SIGMETRICS 01] – NAT effects 11

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 12

Baseline Experiments • Locality-disruption attacks Total hit ratio = Including attackers’ requests and regular users’ requests Stealthy! (4%) Small percent of malicious requests can significantly degrade the overall hit ratio 13

Baseline Experiments • False-locality attacks Total hit ratio is not a good indicator for attacks 14

Byte damage ratio = BHR(n)—byte hit ratio of regular clients without attacks BHR(a)—byte hit ratio of regular clients with attacks 15

Replacement Algorithms • Locality-disruption attacks LRU and LFU are more resilient to attacks, but still can not protect cache from pollution 16

Outline • • Motivation Pollution Attacks Evaluation of Pollution Effects Counter-Pollution Techniques & Evaluation • Conclusion 17

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 18

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) 19

Evaluation of Pollution Detection • Results for false-locality attacks, more in paper For attacker’s file detection: True positive ratio = 20

Implementation • Realize the counter-pollution mechanisms • Code and more details http: //networks. cs. northwestern. edu/AE/ 21

Conclusions • Propose and evaluate two classes of attacks: locality-disruption and falselocality 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 22

Thank You ! Questions? 23