Leveraging Active Decremental TTL Measuring for Flexible and Efficient NAT identification

Malicious attacks can be launched by misusing the network address translation technique as a camouflage. To mitigate such threats, network address translation identification is investigated to identify network address translation devices and detect abnormal behaviors. However, existing methods in this field are mainly developed for relatively small-scale networks and work in an offline manner, which cannot adapt to the real-time inference requirements in high-speed network scenarios. In this paper, we propose a flexible and efficient network address translation identification scheme based on actively measuring the distance of a round trip to a target with decremental time-to-live values. The basic intuition is that the incoming and outgoing traffic from a network address translation device usually experiences the different number of hops, which can be discovered by probing with dedicated time-to-live values. We explore a joint effort of parallel transmission, stateless probes, and flexible measuring reuse to accommodate the efficiency of the measuring process. We further accelerate statistical counting with a new sublinear space data structure Bi-sketch. We implement a prototype and conduct real-world deployments with 1000 volunteers in 31 Chinese provinces, which is believed to bring insight for ground truth collection in this field. Experiments on multi-sources datasets show that our proposal can achieve as high precision and recall as 95% with a traffic handling throughput of over 106 pps.