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

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

Outline SAP (Shrew Attack Protection) Design Overview Deployment Consideration Testbed Experiments Simulation Experiments

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

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)

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

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

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

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 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%

Simulation: Throughput and Drop Rate Throughput (in Kbps) Drop Rate (in %) FTP HTTP BGP Attack No-attack 4996 4995 4.5 - 0.2 5.8 RED ~0 3462 ~100 22.7 SAP Un-protected Port Protected Port R = 15Mbps, T = 1sec, L = 0.3sec RED is not enough to mitigate Shrew attack BGP session is closed

Simulation: Throughput and Drop Rate Throughput (in Kbps) Drop Rate (in %) FTP HTTP BGP Attack No-attack 4996 4995 4.5 - 0.2 5.8 RED ~0 3462 ~100 22.7 SAP Un-protected Port 3975 3870 5.4 1784 3.0 6.1 57.0 Protected Port SAP protects legitimate TCP flows from losing multiple packets Thus, enables high throughput in the presence of attack

Simulation: Throughput and Drop Rate Throughput (in Kbps) Drop Rate (in %) FTP HTTP BGP Attack No-attack 4996 4995 4.5 - 0.2 5.8 RED ~0 3462 ~100 22.7 SAP Un-protected Port 3975 3870 5.4 1784 3.0 6.1 57.0 Protected Port 83 76 1.8 3410 8.9 9.1 22 23 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

Simulation: Throughput and Drop Rate Throughput (in Kbps) Drop Rate (in %) FTP HTTP BGP Attack No-attack 4996 4995 4.5 - 0.2 5.8 RED ~0 3462 ~100 22.7 SAP Un-protected Port 3975 3870 5.4 1784 3.0 6.1 57.0 Protected Port 83 76 1.8 3410 8.9 9.1 22 23 75 1760 1.7 3281 9.0 1.1 28 HTTP flows get higher throughput when Shrew attack uses HTTP SAP keeps all sessions alive

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