ACM SIGCOMM Workshop on Information-Centric Networking – 12/08/2013 PIT Overload Analysis in Content Centric Networks Matteo Virgilio, Guido Marchetto, Riccardo Sisto Department of Control and Computer Engineering Politecnico di Torino 1/16
ACM SIGCOMM Workshop on Information-Centric Networking – 12/08/2013 A stateful protocol: the Pending Interest Table Used to store all seen Interests • One entry for each requested piece of content • Multiple Interests for a single name are merged in a single • entry (Interest merging) CCN Router PIT Name Pending Interfaces /acm.org/papers/paperA.pdf/1 etho /acm.org/papers/paperB.pdf/1 eth1 /acm.org/papers/paperA.pdf/2 eth0 /acm.org/papers/paperB.pdf/2 eth1 2/16
ACM SIGCOMM Workshop on Information-Centric Networking – 12/08/2013 Problem Description Malicious users could craft Interests for non existing • resources: Interest Flooding Attack (IFA) – Very long random names – possibly long lifetime values (even hundreads of seconds) Why do we have to consider so “long” requests? The • answer is long-polling! Supporting publish/subscribe paradigm may require to • store long (potentially unanswered) requests for a long period of time No information about when the response will be generated • (routers cannot make any assumption) Simply dropping Interests with high lifetime is too simplistic • 3/16
ACM SIGCOMM Workshop on Information-Centric Networking – 12/08/2013 What has been done in recent literature? A wide part of the research activity focused on privacy and • data integrity issues What about the PIT? • – Some architecture proposals • Bloom filter implementation of the PIT (DiPIT) • Hash based PIT implementation with some interesting variants (Name Prefix Tree encoding) – Reactive algorithms for IFA handling: • Statistics based reaction to attackers activity; • Poseidon Framework (very recent) 4/16
ACM SIGCOMM Workshop on Information-Centric Networking – 12/08/2013 Our contribution Simulation based approach • – we developed a full custom Java ccnSimulator Different target: evaluating attack impact on a real • topology Evaluate different PIT architectures in various network load • (and attack) scenarios 5/16
ACM SIGCOMM Workshop on Information-Centric Networking – 12/08/2013 Simulation scenario Reference topology from Telecom Italia (the most prominent • Italian ISP) 9 milions of subscribers • ADSL with 7Mbps/1Mbps • (downlink/uplink) Zipf content distribution • Metrics gathered • – Chunk retransmission rate at the endpoints Fixed PIT size • – 1 GB 6/16
ACM SIGCOMM Workshop on Information-Centric Networking – 12/08/2013 Attack model Distributed bot net • Different simulation campaigns • 1) Variable lifeTime 2) Variable bandwidth Different URI size • ≈1000 bytes for the SimplePIT case 20 bytes for the HashedPIT case (SHA-1 as hashing algorithm) 7/16
ACM SIGCOMM Workshop on Information-Centric Networking – 12/08/2013 Attacker ’s transmission efficiency SimplePIT HashedPIT, DiPIT Interest Header Interest Header (20 bytes) (20 bytes) Resource name Resource name (1000 bytes) (20 bytes) Attack efficiency Attack efficiency 1000 bytes 20 bytes 98 % 50 % ( 20 1000 ) bytes ( 20 20 ) bytes 8/16
ACM SIGCOMM Workshop on Information-Centric Networking – 12/08/2013 Simulation Results (1) AttackSettings SimplePIT HashedPIT DiPIT Retransmissions /RAM usage Retransmissions/RAM usage Retransmissions /RAM usage 0 49 MB 0 25 MB 0.01 % 1 GB Band = 100 Mbps LifeTime= 4 sec 0 245 MB 0 125 MB 2.42 % 1 GB Band = 500 Mbps LifeTime= 4 sec 0 980 MB 0 500 MB 87.6 % 1 GB Band = 2 Gbps LifeTime= 4 sec 15 % FULL 83 % FULL 90 % 1 GB Band = 4 Gbps LifeTime= 4 sec 0 735 MB 0 375 MB 21 % 1 GB Band = 100 Mbps LifeTime= 60 sec 37 % FULL 0 750 MB 86 % 1 GB Band = 100 Mbps LifeTime= 120 sec 52 % FULL ∞ FULL 88 % 1 GB Band = 100 Mbps LifeTime= 180 sec 9/16
ACM SIGCOMM Workshop on Information-Centric Networking – 12/08/2013 Simulation Results (1) AttackSettings SimplePIT HashedPIT DiPIT Retransmissions /RAM usage Retransmissions/RAM usage Retransmissions /RAM usage 0 49 MB 0 25 MB 0.01 % 1 GB Band = 100 Mbps LifeTime= 4 sec 0 245 MB 0 125 MB 2.42 % 1 GB Band = 500 Mbps LifeTime= 4 sec 0 980 MB 0 500 MB 87.6 % 1 GB Band = 2 Gbps LifeTime= 4 sec 15 % FULL 83 % FULL 90 % 1 GB Band = 4 Gbps LifeTime= 4 sec 0 735 MB 0 375 MB 21 % 1 GB Band = 100 Mbps LifeTime= 60 sec 37 % FULL 0 750 MB 86 % 1 GB Band = 100 Mbps LifeTime= 120 sec 52 % FULL ∞ FULL 88 % 1 GB Band = 100 Mbps LifeTime= 180 sec 10/16
ACM SIGCOMM Workshop on Information-Centric Networking – 12/08/2013 Simulation Results (1) AttackSettings SimplePIT HashedPIT DiPIT Retransmissions /RAM usage Retransmissions/RAM usage Retransmissions /RAM usage 0 49 MB 0 25 MB 0.01 % 1 GB Band = 100 Mbps LifeTime= 4 sec 0 245 MB 0 125 MB 2.42 % 1 GB Band = 500 Mbps LifeTime= 4 sec 0 980 MB 0 500 MB 87.6 % 1 GB Band = 2 Gbps LifeTime= 4 sec 15 % FULL 83 % FULL 90 % 1 GB Band = 4 Gbps LifeTime= 4 sec 0 735 MB 0 375 MB 21 % 1 GB Band = 100 Mbps LifeTime= 60 sec 37 % FULL 0 750 MB 86 % 1 GB Band = 100 Mbps LifeTime= 120 sec 52 % FULL ∞ FULL 88 % 1 GB Band = 100 Mbps LifeTime= 180 sec 11/16
ACM SIGCOMM Workshop on Information-Centric Networking – 12/08/2013 Simulation Results (1) AttackSettings SimplePIT HashedPIT DiPIT Retransmissions /RAM usage Retransmissions/RAM usage Retransmissions /RAM usage 0 49 MB 0 25 MB 0.01 % 1 GB Band = 100 Mbps LifeTime= 4 sec 0 245 MB 0 125 MB 2.42 % 1 GB Band = 500 Mbps LifeTime= 4 sec 0 980 MB 0 500 MB 87.6 % 1 GB Band = 2 Gbps LifeTime= 4 sec 15 % FULL 83 % FULL 90 % 1 GB Band = 4 Gbps LifeTime= 4 sec 0 735 MB 0 375 MB 21 % 1 GB Band = 100 Mbps LifeTime= 60 sec 37 % FULL 0 750 MB 86 % 1 GB Band = 100 Mbps LifeTime= 120 sec 52 % FULL ∞ FULL 88 % 1 GB Band = 100 Mbps LifeTime= 180 sec 12/16
ACM SIGCOMM Workshop on Information-Centric Networking – 12/08/2013 Simulation Results (2) • Settings: Band = 100 Mbps, LifeTime = 180 sec • Settings: Band = 4 Gbps, LifeTime = 4 sec 13/16
ACM SIGCOMM Workshop on Information-Centric Networking – 12/08/2013 Conclusion All the architectures work properly in normal network • conditions and also in presence of low intensity attack HashedPIT is the most affected PIT in our context • Other scenarios could be designed to worsen SimplePIT too • – Distribute more zombies around the network; – Combine both high bandwidth and high lifetime to maximize the attack effectiveness; – … Scalable and robust solutions are needed to ensure an • adequate level of confidence to the CCN paradigm. 14/16
ACM SIGCOMM Workshop on Information-Centric Networking – 12/08/2013 Future contribution Very recent solutions have been proposed to mitigate the • impact of Interest Flooding Attacks Our plan for the future is to evaluate them in our scenarios • in terms of: – Resilience – CPU usage – Memory usage 15/16
ACM SIGCOMM Workshop on Information-Centric Networking – 12/08/2013 Thank you for the attention! 16/16
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