A2C: Anti-Attack Counters for Traffic Measurement

Flow-level sampling methods have been widely studied and extensively employed in network traffic measurement systems. However, traffic anomalies are becoming more prevalent and severe in the Internet, which pose great challenges to the traffic measurement. Existing solutions targeted at such scenario have either low accuracy or high memory usage. In this paper, we propose a two-stage sampling approach-Anti Attack Counters (A2C) and an efficient parameter adapting method to solve the problem. The proposed sampling mechanism can adapt to the network condition automatically and collect more information even under severe traffic attacks. Theoretical analysis on accuracy and resource requirement is presented in our work. Furthermore, we validate our approach using both synthetic and real traces. The experimental results demonstrate that A2C is of high resilience while providing significantly improved measurement accuracy with reduced memory occupation comparing with other existing anti-attack countermeasures.