Towards Street-Level Client-Independent IP Geolocation

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3 D 1 D 2 R 1 R 3 D 4 D 3 R 2 V 1 V 2 Vantage Point Landmark Target Router Figure 2: An example of measuring the delay between landmark and target Figure 1: An example of intersection created by distance constraints demonstrated that 2/3 c is a loose upper bound in practice due to transmission delay, queuing delay etc. [15, 17]. Based on this observation, we adopt 4/9 c from [17] as the converting factor between measured delay and geographical distance. We also demonstrate in Section 4, by using this converting factor, we are always capable of yielding a viable area covering the targeted IP. Once we establish the distance from each vantage point, i.e., ping server, to the target, we use multilateration to build an intersection that covers the target using known locations of these servers. In particular, for each vantage point, we draw a ring centered at the vantage point, with a radius of the measured distance between the vantage point and the target. As we show in Section 4, this approach indeed allows us to always find a region that covers the targeted IP. Figure 1 illustrates an example. It geolocates a collected target (we will elaborate the way of collecting the targets in the wild in Section 4.1.2) whose IP address is and whose postal address is 1850, K Street NW, Washington DC, DC, We draw rings centered at the locations of our vantage points. The radius of each ring is determined by the measured distance between the vantage point (the center of this ring) and the target. Finally, we geolocate this IP in an area indicated by the shaded region, which covers the target, as shown in Figure 1. Thus, by applying the CBG approach, we manage to geolocate a region where the targeted IP resides. According to [17, 24], CBG achieves a median error between 143 km and 228 km distance to the target. Since we strive for a much higher accuracy, this is only the starting point for our approach. To that end, we depart from pure delay measurements and turn to the use of external information available on the Web. Our next goal is to further determine a subset of ZIP Codes, i.e., smaller regions that belong to the bigger region found via the CBG approach. Once we find the set of ZIP Codes, we will search for additional websites served within them. Our goal is to extract and verify the location information about these locally-hosted Web services. In this way, we obtain a number of accurate Web-based landmarks that we will use in Tiers 2 and 3 to achieve high geolocation accuracy. To find a subset of ZIP Codes that belong to the given region, we proceed as follows. We first determine the center of the intersection area. Then, we draw a ring centered in the intersection center with a diameter of 5 km. Next, we sample 10 latitude and longitude pairs at the perimeter of this ring, by rotating by 36 degrees between each point. For the 10 initial points, we verify that they belong to the intersection area as follows. Denote byu the set of latitude and longitude pairs to be verified. Next, denote byv the set of all vantage points, i.e., ping servers, with known location. Each vantage point v i is associated with the measured distance between itself and the target, denoted by r i. We wish to find all u U that satisfy distance(u,v i ) r i for all v i V The distance function here is the great-circle distance [23], which takes into account the earth s sphericity and is the shortest distance between any two points on the surface of the earth measured along a path on the surface of the earth. We repeat this procedure by further obtaining 10 additional points by increasing the distance from the intersection center by 5 km in each round (i.e., to 10 km in the second round, 15 km in the third etc.). The procedure stops when not a single point in a round belongs to the intersection. In this way, we obtain a sample of points from the intersection, which we convert to ZIP Codes using a publicly available service [4]. Thus, with the set of ZIP Codes belonging to the intersection, we proceed to Tier 2. 3

4 2.2 Tier 2 Here, we attempt to further reduce the possible region where the targeted IP is located. To that end, we aim to find Web-based landmarks that can help us achieve this goal. We explain the methodology for obtaining such landmarks in Section 3. Although these landmarks are passive, i.e., we cannot actively send probes to other Internet hosts using them, we use the traceroute program to indirectly estimate the delay between landmarks and the target. Learning from [11] that the more traceroute servers we use, the more direct a path between a landmark and the target we can find, we first send traceroute probes to the landmark (the empty circle in Figure 2) and the target (the triangle in Figure 2) from all traceroute servers (the solid squares V 1 and V 2 in Figure 2). For each vantage point, we then find the closest common router to the target and the landmark, shown as R 1 and R 2 in Figure 2, on the routes towards both the landmark and the target. Next, we calculate the latency between the common router and the landmark (D 1 and D 3 in Figure 2) and the latency between the common router and the target (D 2 and D 4 in Figure 2). We finally select the sum (D) of two latencies as the delay between landmark and target. In the example above, from V 1 s point of view, the delayd between the target and the landmark isd = D 1 +D 2, while fromv 2 s perspective, the delay D isd = D 3 +D 4. Since different traceroute servers have different routes to the destination, the common routers are not necessarily the same for all traceroute servers. Thus, each vantage point (a traceroute server) can estimate a different delay between a Web-based landmark and the target. In this situation, we choose the minimum delay from all traceroute servers measurements as the final estimation of the latency between the landmark and the target. In Figure 2, since the path between landmark and target from V 1 s perspective is more direct than that from V 2 s (D 1 +D 2 < D 3 +D 4 ), we will consider the sum ofd 1 andd 2 (D 1 +D 2 ) as the final estimation. Routers in the Internet may postpone responses. Consequently, if the delay on the common router is inflated, we may underestimate the delay between landmark and target. To examine the quality of the common router we use, we first traceroute different landmarks we collected previously and record the paths between any two landmarks, which also branch at that router. We then calculate the great circle distance [23] between two landmarks and compare it with their measured distance. If we observe that the measured distance is smaller than the calculated great circle distance for any pair of landmarks, we label this router as inflating, record this information, and do not consider its path (and the corresponding delay) for this or any other measurement. Through this process, we can guarantee that the esti- Figure 3: An example of shrinking the intersection mated delay between a landmark and the target is not underestimated. Nonetheless, such estimated delay, while converging towards the real latency between the two entities, is still usually larger. Hence, it can be considered as the upper bound of the actual latency. Using multilateration with the upper bound of the distance constraints, we further reduce the feasible region using the new tier 2 and the old tier 1 constraints. Figure 3 shows the zoomed-in subset of the constrained region together with old tier 1 constraints, marked by thick lines, and new tier 2 constraints, marked by thin lines. The figure shows a subset of sampled landmarks, marked by the solid dots, and the IP that we aim to geolocate, marked by a triangle. The tier 1 constrained area contains 257 distinctive ZIP Codes, in which we are able to locate and verify 930 Web-based landmarks. In the figure, we show only a subset of 161 landmarks for a clearer presentation. Some sampled landmarks lie outside the original tier 1 level intersection. This happens because the sampled ZIP Codes that we discover at the borders of the original intersection area typically spread outside the intersection as well. Finally, the figure shows that the tier 2 constrained area is approximately one order of magnitude smaller than the original tier 1 area. 2.3 Tier 3 In this final step, our goal is to complete our geolocation of the targeted IP address. We start from the region constrained in Tier 2, and aim to find all ZIP Codes in this region. To this end, we repeat the sampling procedure deployed in the Tier 2. This time from the center of the Tier 2 constrained intersection area, and at a higher granularity. In particular, we extend the radius distance by 1 km in each step, and apply a rotation angle of 10 degrees. Thus, we achieve 36 points in each round. We apply the same stopping criteria, i.e., when no points in a round belong to the intersection. This finer-grain sam- 4

5 Measured distance [km] Geographical distance [km] Figure 5: Measured distance vs. geographical distance. Figure 4: An example of associating a landmark with the target as the result pling process enables us to discover all ZIP Codes in the intersection area. For ZIP Codes that were not found in the previous step, we repeat the landmark discovery process (Section 3). Moreover, to obtain the distance estimations between newly discovered landmarks and the target, we apply the active probing traceroute process explained above. Finally, knowing the locations of all Web-based landmarks and their estimated distances to the target, we select the landmark with the minimum distance to the target, and associate the target s location with it. While this approach may appear ad hoc, it signifies one of the key contributions of our paper. We find that on the smallerscale, relative distances are preserved by delay measurements, overcoming many of fundamental inaccuracies encountered in the use of absolute measurements. For example, a delay of several milliseconds, commonly seen at the last mile, could place an estimate of a scheme that relies on absolute delay measurements hundreds of kilometers away from the target. On the contrary, selecting the closest node in an area densely populated with landmarks achieves remarkably accurate estimates, as we show below in our example case, and demonstrate systematically in Section 4 via large-scale analysis. Figure 4 shows the striking accuracy of this approach. We manage to associate the targeted IP location with a landmark which is across the street, i.e., only km distant from the target. We analyze this result in more detail below. Here, we provide the general statistics for the Tier 3 geolocation process. In this last step, we discover 26 additional ZIP Codes and 203 additional landmarks in the smaller Tier 2 intersection area. We then associate the landmark, which is at 1776 K Street Northwest, Washington, DC and has a measured distance of 10.6 km, yet a real geographical distance of km, with the target. To clearly show the association, Figure 4 zooms into a very finer-grain street level in which the constrained rings and relatively more distant landmarks are not shown The Power of Relative Network Distance Here, we explore how the relative network distance approach achieves such good results. Figure 5 sheds more light on this phenomenon. We examine the 13 landmarks within 0.6 km of the target shown in Figure 4. For each landmark, we plot the distance between the target and the Web-based landmarks (y-axis) (measured via the traceroute approach) as a function of the actual geographical distance between the landmarks and the target (x-axis). The first insight from the figure is that there is indeed a significant difference between measured distance, i.e., their upper bounds, and the real distances. This is not a surprise. A path between a landmark, over the common router, to the destination (Figure 2) can often be circuitous and inflated by queuing and processing delays, as demonstrated in [17]. Hence, the estimated distance dramatically exceeds the real distance, by approximately three orders of magnitude in this case. However, Figure 5 shows that the distance estimated via network measurements (y-axis) is largely in proportion with the actual geographical distance. Thus, despite the fact that the direct relationship between the real geographic distance and estimated distance is inevitably lost in inflated network delay measurements, the relative distance is largely preserved. This is because the network paths that are used to estimate the distance between landmarks and the target share vastly common links, hence experience similar transmissionand queuing-delay properties. Thus, selecting a landmark with the smallest delay is an effective approach, as we also demonstrate later in the text. 5

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