The CAIDA annual report (quite a bit later than usual this year due to an unprecedented level of activity in 2024 which we will report on earlier next year!) summarizes CAIDA’s activities for 2023 in the areas of research, infrastructure, data collection and analysis. The executive summary is excerpted below:
Read the rest of this entry »
Users can now request access to the CAIDA BGP2GO (https://bgp2go.caida.org) platform. BGP2GO lets users find the MRT files that contain a specific resource and thus avoid the download and processing of unrelated data. Users can compile a customize list of relevant MRT files, share that exact list with others, or stream the matching MRT files (e.g., using BGPStream).
Public BGP route collectors receive update messages from over 1,000 BGP routers worldwide. These updates are archived and made available for download and analysis. However, the data is organized in a way that often requires downloading vast amounts of unrelated information making it increasingly difficult to focus on the needles in the haystack.
Imagine you’re a network operator announcing a new prefix and you want to analyze its propagation. You may not know exactly which collector or MRT file contains the relevant data, forcing you to download and sift through unnecessary information.
BGP2GO solves this problem by allowing users to easily select only the files they need, saving significant time and effort.
We have developed a comprehensive index of all prefixes, ASNs, and communities across RouteViews update files (https://archive.routeviews.org), along with BGP2GO, a user interface that allows you to easily select the specific files you need (https://bgp2go.caida.org)
Let’s walk through a real-world example. Suppose you’re a network operator advertising a prefix and want to examine how it propagates across RouteViews collectors. Using the PEERING testbed (https://peering.ee.columbia.edu/), we performed a controlled advertisement of the prefix 184.164.246.0/24 during August 2024.
In this case, we search for the prefix 184.164.246.0/24 in BGP2GO, filtering for data from August 2024. The platform identifies 33,251 announcements and withdrawals, spread across 746 files (2.32GB) from 26 collectors. This curated selection lets us focus only on the data we need, saving time and resources.
https://bgp2go.caida.org/details?pre=184.164.246.0/24&years=2024&months=8
Once the relevant MRT files are selected, you can stream them for further processing using BGPStream. Clicking the BGPSTREAM button in the top right corner, right above the “collectors” chart gives instructions on how to stream the files in your terminal.
For this example, we use the following command (see step 4a above) in the terminal to process the relevant files:
bgpreader -k 184.164.246.0/24 -d csvfile -o csv-file="bgp2go.csv"
This command leverages bgpreader to read the files and output the lines that pertain to the resource (prefix 184.164.246.0/24) . The following screenshot shows an excerpt of routes related to the prefix 184.164.246.0/24 extracted from all files that contain this prefix in August 2024.
To learn more about the bgpreader command and its options, visit the BGPStream website (https://bgpstream.caida.org/) .
To request access to the BGP2GO platform, a user should first create an account with the CAIDA Services Single Sign On (SSO) system ( https://auth.caida.org/realms/CAIDA/account ) by providing basic information and undergoing authentication via Keycloak. After authentication, a user can request access to the BGP2Go platform (which requires a CAIDA-authorized account with bgp2go-api:read role) by going to https://bgp2go.caida.org/ and filling out the request form. If you have any questions or problems, please contact us at data-info@caida.org
We are seeking beta users for our new Anonymized Two-Way Passive Trace dataset, captured on a 100 GB link between Los Angeles and San Jose. Beginning in April 2024, we have been capturing a one-hour trace each month. To protect privacy, we strip all packet payloads after the layer 4 headers, and anonymize IP (v4 and v6) addresses with CryptoPan. The monthly data is provided in two separate files, one for each direction of traffic.
This dataset includes the following metadata fields:
The data is stored in our Swift OpenStack object storage. Each one-directional anonymized pcap file captured monthly is approximately 1TB in size, so users will need more than 2TB of space to download the entire one-hour capture. For those without access to such storage and/or processing capacity, contact us and we will discuss other alternatives. We are also releasing statistical information for each hourly trace.
Academic researchers can request access to the data by filling out and submitting the request form.
We will prioritize users who:
We are excited to announce the beta testing phase of the Facilitating Advances in Network Topology Analysis (FANTAIL) platform (https://www.caida.org/projects/fantail/), a cutting-edge topology query system designed to search vast archives of raw Internet end-to-end path (traceroute) measurement data. FANTAIL is poised to support and advance various research domains within the Computer and Information Science and Engineering (CISE) field that heavily rely on the emerging sub-discipline of Internet cartography. Key areas of focus include:
FANTAIL consists of four components:
The system’s central data type is the traceroute path, representing the inferred IP-level Internet path that network traffic would take between two hosts, from the measurement vantage point to the destination, as determined with the traceroute technique by scamper (https://catalog.caida.org/software/scamper). FANTAIL leverages annotated and indexed data generated through the utilization of Spark, SQLite, and Elasticsearch, originating from CAIDA Internet traceroute probing data dating back to 2015.
Academic researchers interested in accessing the platform can request access by emailing fantail-info@caida.org.
We will prioritize users who can commit to regular feedback and participation throughout the beta testing period.
This is the third in a series of essays (two earlier blog posts [1, 2]) about CAIDA’s new effort to reduce the barrier to performing a variety of Internet measurements.
We were recently asked about running Trufflehunter, which infers the usage properties of rare domain names on the Internet by cache snooping public recursive resolvers, on Ark. The basic idea of Trufflehunter is to provide a lower bound of the use of a domain name by sampling caches of large recursive resolvers. One component of Trufflehunter is to identify the anycast instances that would answer a given vantage point’s DNS queries. Trufflehunter uses a series of TXT queries to obtain the anycast instance of a given public recursive resolver, and considers four large public recursive resolvers: Cloudflare (1.1.1.1), Google (8.8.8.8), Quad9 (9.9.9.9), and OpenDNS (208.67.220.220). The original paper describes the queries in section 4.1, which we summarize below, highlighting the interesting parts of each response with blue color.
Cloudflare returns an airport code representing the anycast deployment location used by the VP:
$ host -c ch -t txt id.server 1.1.1.1
id.server descriptive text "AKL"
Google returns an IP address representing the anycast deployment used by the VP, which can be mapped to an anycast deployment location with a second query:
$ host -t txt o-o.myaddr.l.google.com 8.8.8.8
o-o.myaddr.l.google.com descriptive text "172.253.218.133"
o-o.myaddr.l.google.com descriptive text "edns0-client-subnet <redacted>/24"
$ host -t txt locations.publicdns.goog
locations.publicdns.goog descriptive text "34.64.0.0/24 icn " ... "172.253.218.128/26 syd " "172.253.218.192/26 cbf " ...
Quad9 returns the hostname representing the resolver that provides the answer:
$ host -c ch -t txt id.server 9.9.9.9
id.server descriptive text "res100.akl.rrdns.pch.net"
and finally, OpenDNS returns a bunch of information in a debugging query, which includes the server that handles the query:
$ host -t txt debug.opendns.com 208.67.220.220
debug.opendns.com descriptive text "server r2004.syd"
debug.opendns.com descriptive text "flags 20040020 0 70 400180000000000000000007950800000000000000"
debug.opendns.com descriptive text "originid 0"
debug.opendns.com descriptive text "orgflags 2000000"
debug.opendns.com descriptive text "actype 0"
debug.opendns.com descriptive text "source <redacted>:42845"
Each of these queries requires a slightly different approach to extract the location of the anycast instance. Our suggestion is for experimenters to use our newly created python library, which provides programmatic access to measurement capabilities of Ark VPs (described in two earlier blog posts [1, 2]). The code for querying the recursive resolvers from all VPs is straight forward, and is shown below. First, on lines 15-27, we get the Google mapping, so that we can translate the address returned to an anycast location. This query requires TCP to complete, as the entry is larger than can fit in a UDP payload (the response is 9332 bytes at the time of writing this blog). This query is synchronous (we wait for the answer before continuing, because the google queries that follow depend on the mapping) and is issued using a randomly selected Ark VP. Then, on lines 29-38, we issue the four queries to each of the anycasted recursive resolvers from each VP. These queries are asynchronous; we receive the answers as each Ark VP obtains a response. On lines 40-82, we process the responses, storing the results in a multi-dimensional python dictionary, which associates each Ark VPs with their anycast recursive resolver location, as well as the RTT between asking the query and obtaining the response. Finally, on lines 84-96, we dump the results out in a nicely formatted table that allows us to spot interesting patterns.
01 import argparse
02 import datetime
03 import ipaddress
04 import random
05 import re
06 from scamper import ScamperCtrl
07
08 def _main():
09 parser = argparse.ArgumentParser(description='get public recursive locs')
10 parser.add_argument('sockets')
11 args = parser.parse_args()
12
13 ctrl = ScamperCtrl(remote_dir=args.sockets)
14
15 # pick an ark VP at random to issue the query that gets the mapping
16 # of google recursive IP to location
17 goog_nets = {}
18 obj = ctrl.do_dns('locations.publicdns.goog',
19 inst=random.choice(ctrl.instances()),
20 qtype='txt', tcp=True, sync=True)
21 if obj is None or len(obj.ans_txts()) == 0:
22 print("could not get google mapping")
23 return
24 for rr in obj.ans_txts():
25 for txt in rr.txt:
26 net, loc = txt.split()
27 goog_nets[ipaddress.ip_network(net)] = loc
28
29 # issue the magic queries to get the instance that answers the query
30 for inst in ctrl.instances():
31 ctrl.do_dns('o-o.myaddr.l.google.com', server='8.8.8.8', qtype='txt',
32 attempts=2, wait_timeout=2, inst=inst)
33 ctrl.do_dns('id.server', server='1.1.1.1', qclass='ch', qtype='txt',
34 attempts=2, wait_timeout=2, inst=inst)
35 ctrl.do_dns('id.server', server='9.9.9.9', qclass='ch', qtype='txt',
36 attempts=2, wait_timeout=2, inst=inst)
37 ctrl.do_dns('debug.opendns.com', server='208.67.220.220', qtype='txt',
38 attempts=2, wait_timeout=2, inst=inst)
39
40 # collect the data
41 data = {}
42 for obj in ctrl.responses(timeout=datetime.timedelta(seconds=10)):
43 vp = obj.inst.name
44 if vp not in data:
45 data[vp] = {}
46 dst = str(obj.dst)
47 if dst in data[vp]:
48 continue
49 data[vp][dst] = {}
50 data[vp][dst]['rtt'] = obj.rtt
51
52 for rr in obj.ans_txts():
53 for txt in rr.txt:
54 if dst == '8.8.8.8':
55 # google reports an IPv4 address that represents
56 # the site that answers the query. we then map
57 # that address to a location using the mapping
58 # returned by the locations TCP query.
59 try:
60 addr = ipaddress.ip_address(txt)
61 except ValueError:
62 continue
63 for net, loc in goog_nets.items():
64 if addr in net:
65 data[vp][dst]['loc'] = loc
66 break
67 elif dst == '1.1.1.1':
68 # Cloudflare replies with a single TXT record
69 # containing an airport code
70 data[vp][dst]['loc'] = txt
71 elif dst == '9.9.9.9':
72 # Quad9 reports a hostname with an embedded
73 # airport code.
74 match = re.search("\\.(.+?)\\.rrdns\\.pch\\.net", txt)
75 if match:
76 data[vp][dst]['loc'] = match.group(1)
77 elif dst == '208.67.220.220':
78 # opendns reports multiple TXT records; we want the one
79 # that looks like "server r2005.syd"
80 match = re.search("^server .+\\.(.+?)$", txt)
81 if match:
82 data[vp][dst]['loc'] = match.group(1)
83
84 # format the output
85 print("{:15} {:>13} {:>13} {:>13} {:>13}".format(
86 "# vp", "google", "couldflare", "quad9", "opendns"))
87 for vp, recs in sorted(data.items()):
88 line = f"{vp:15}"
89 for rec in ('8.8.8.8', '1.1.1.1', '9.9.9.9', '208.67.220.220'):
90 cell = ""
91 if 'loc' in recs[rec]:
92 rtt = recs[rec]['rtt']
93 loc = recs[rec]['loc']
94 cell = f"{loc} {rtt.total_seconds()*1000:5.1f}"
95 line += f" {cell:>13}"
96 print(line)
97
98 if __name__ == "__main__":
99 _main()
The code runs quickly — no longer than 10 seconds (if one of the VPs is slow to report back), illustrating the capabilities of the Ark platform.
The output of running this program is shown below. We have highlighted cells where the RTT was at least 50ms larger than the minimum RTT to any of the large recursive resolvers for the given VP. These cells identify low-hanging fruit for operators, who could examine BGP routing policies with the goal of selecting better alternative paths.
# vp google couldflare quad9 opendns
abz-uk.ark lhr 24.4 LHR 16.5 lhr 16.9 lon 16.4
abz2-uk.ark lhr 23.4 MAN 8.7 man 8.4 man1 8.5
acc-gh.ark jnb 246.7 JNB 167.3 acc 0.5 cpt1 236.6
adl-au.ark mel 28.1 ADL 5.5 syd 22.6 mel1 14.0
aep-ar.ark scl 29.4 EZE 8.0 qaep2 4.6 sao1 34.0
aep2-ar.ark scl 38.2 EZE 6.6 qaep2 5.7 sao1 42.5
akl-nz.ark syd 26.9 AKL 2.6 akl2 4.0 syd 26.1
akl2-nz.ark syd 29.2 AKL 4.9 akl2 4.0 syd 26.9
ams-gc.ark grq 5.0 FRA 6.3 ams 0.8 ams 1.1
ams3-nl.ark grq 6.0 AMS 2.1 ams 1.3 ams 1.9
ams5-nl.ark grq 5.1 AMS 1.2 ams 1.9 ams 1.7
ams7-nl.ark grq 6.0 AMS 2.4 ams 1.8 ams 2.3
ams8-nl.ark grq 14.8 AMS 11.5 fra 17.7 ams 11.8
arn-se.ark lpp 9.7 ARN 2.0 arn 0.8 cph1 10.5
asu-py.ark scl 55.4 EZE 22.6 asu 0.8 sao1 57.0
atl2-us.ark atl 9.8 DFW 23.6 qiad3 20.5 atl1 4.6
atl3-us.ark atl 22.7 ATL 13.1 atl 17.0 atl1 19.7
aus-us.ark dfw 80.7 DFW 56.6 dfw 46.6 dfw 17.6
avv-au.ark mel 142.3 MEL 9.4 syd 22.6 mel1 8.5
bcn-es.ark mad 38.0 BCN 13.3 bcn 0.5 mad1 9.8
bdl-us.ark iad 13.3 EWR 5.0 lga 4.9 ash 10.6
bed-us.ark iad 30.6 BOS 15.2 bos 13.5 bos1 19.8
beg-rs.ark mil 62.5 BEG 1.2 beg 0.7 otp1 12.9
bfi-us.ark dls 11.7 SEA 3.8 xsjc1 96.9 sea 10.5
bjl-gm.ark bru 87.5 CDG 71.1 bjl 6.7 cdg1 73.4
bna-us.ark atl 11.4 BNA 9.0 atl 9.5 atl1 9.3
bna2-us.ark cbf 25.5 ORD 19.7 ord 12.8 chi 12.8
bos6-us.ark iad 17.6 BOS 5.6 qiad3 15.5 bos1 5.7
bre-de.ark grq 21.4 TXL 23.1 ber 17.2 ams 23.7
btr-us.ark dfw 20.3 ORD 29.5 dfw 13.2 dfw 11.4
bwi2-us.ark iad 12.7 IAD 4.4 qiad3 3.6 rst1 3.1
cdg-fr.ark mil 24.9 MRS 1.5 mrs 1.0 mrs1 11.6
cdg3-fr.ark bru 8.5 CDG 3.9 ams 13.7 cdg1 5.5
cgs-us.ark iad 4.5 EWR 7.9 iad 2.2 ash 2.1
cjj-kr.ark hkg 63.4 qhnd2 70.7 hkg 47.3
cld4-us.ark lax 22.7 LAX 15.7 bur 104.1 lax 14.3
cld5-us.ark lax 52.6 LAX 20.7 bur 103.1 lax 21.9
cld6-us.ark lax 14.5 LAX 5.2 qlax1 7.0 lax 6.0
cos-us.ark cbf 22.9 DEN 7.3 ord 36.0 den1 12.6
dar-tz.ark jnb 224.6 NBO 14.0 dar 1.0 jnb 51.1
dar2-tz.ark jnb 223.0 DAR 0.9 dar 0.9 jnb 48.0
dmk-th.ark sin 32.2 SIN 28.0 bkk 2.4 nrt2 95.0
dtw2-us.ark cbf 18.1 DTW 4.6 dtw 4.2 chi 10.7
dub-ie.ark lhr 18.9 DUB 1.2 dub 0.7 dub1 0.7
dub2-ie.ark lhr 18.7 DUB 1.8 dub 1.1 dub1 1.2
dub3-ie.ark lhr 35.5 ZRH 45.6 dub 16.9 dub1 11.6
ens-nl.ark grq 9.3 AMS 5.2 ams 4.0 ams 4.9
eug-us.ark dls 15.7 SJC 18.3 sea 6.5 sea 6.5
fra-gc.ark fra 9.1 FRA 1.1 fra 1.1 fra 1.1
gig-br.ark gru 125.2 GIG 2.2 qrio1 1.4 rio1 1.1
gva-ch.ark ZRH 5.3 gva 1.0 fra 10.8
gye-ec.ark chs 128.7 MIA 66.8 quio2 10.7 mia 77.1
ham-de.ark grq 19.7 HAM 8.5 ber 11.0 fra 17.7
her2-gr.ark mil 64.9 ath 7.5 mil1 32.6
hkg4-cn.ark tpe 13.6 HKG 1.0 qhkg3 4.9 hkg 9.5
hkg5-cn.ark hkg 16.3 HKG 2.6 qhkg3 3.9 hkg 3.6
hlz2-nz.ark syd 40.9 AKL 16.9 akl 13.8 syd 38.2
hnd-jp.ark nrt 6.5 NRT 3.4 qhnd2 80.3 nrt 3.6
hnl-us.ark dls 82.3 HNL 1.1 sea 75.2 sea 75.3
iev-ua.ark waw 45.5 FRA 29.8 qwaw2 14.3 wrw1 14.9
igx2-us.ark iad 16.1 EWR 16.9 qiad3 11.5 atl1 11.8
ind-us.ark atl 25.1 IND 1.1 ord 8.5 atl1 11.0
ixc-in.ark del 78.8 DEL 0.8 qsin1 0.7 mum2 0.7
jfk-us.ark iad 9.6 EWR 1.5 lga 0.6 nyc 1.0
kgl-rw.ark jnb 231.6 NBO 15.0 kgl 3.3 jnb 73.6
ktm-np.ark del 98.6 DEL 30.4 ktm 7.6 mum1 94.4
las-us.ark lax 15.5 LAX 8.4 pao 15.9 lax 8.2
lax3-us.ark lax 10.5 LAX 3.0 bur 2.0 lax 2.0
lcy2-uk.ark lhr 11.5 MAN 7.4 lhr 11.2 lon 2.2
lej-de.ark fra 21.2 FRA 9.7 fra 12.5 fra 9.6
lex-us.ark iad 17.9 IAD 15.7 iad 17.3 atl1 25.9
lgw-uk.ark lhr 31.3 LHR 24.2 lhr 24.6 lon 23.6
lhe2-pk.ark dia 195.0 KHI 35.0 qsin4 113.1 sin 130.6
lis-pt.ark mad 33.0 LIS 4.5 lis 3.9 mad1 18.0
lke2-us.ark dls 21.7 SEA 21.4 sea 15.1 sea 11.7
lun-zm.ark jnb 189.9 JNB 32.2 jnb 23.3 jnb 23.3
lwc-us.ark tul 18.0 MCI 1.4 xsjc1 106.3 dfw 10.4
lwc2-us.ark dfw 24.4 DFW 15.7 qlax1 46.9 dfw 16.5
mdw-us.ark cbf 25.7 ORD 7.0 ord 3.4 chi 4.4
med2-co.ark mrn 96.4 MIA 50.7 qbog1 22.7 mia 48.9
mhg-de.ark fra 30.7 FRA 19.1 fra 21.4 fra 18.4
mia-gc.ark mrn 19.7 MIA 1.1 mia 1.0 mia 0.6
mnl-ph.ark hkg 87.9 MNL 1.8 qsin1 50.8 hkg 75.8
mnz-us.ark iad 13.4 IAD 5.8 qiad3 5.2 rst1 4.0
mru-mu.ark jnb 206.1 JNB 43.1 mru 1.0 jnb 43.3
msy-us.ark dfw 34.4 DFW 21.9 qlax1 55.2 dfw 23.0
mty-mx.ark tul 44.1 MFE 4.5 qiad3 39.6 dfw 16.7
muc-de.ark fra 30.3 FRA 9.1 fra 8.8 fra 8.9
muc3-de.ark zrh 25.9 MUC 6.1 fra 11.0 fra 12.2
nap2-it.ark mil 37.8 MXP 19.8 fra 38.0 mil1 15.2
nbo-ke.ark jnb 220.5 NBO 2.4 nbo 2.2 jnb 60.7
nic-cy.ark mil 61.3 LCA 1.0 mrs 35.2 mrs1 35.4
nrn-nl.ark grq 8.6 AMS 5.5 ams 2.9 ams 3.1
nrt-jp.ark nrt 4.8 NRT 1.2 hkg 101.9 nrt2 1.0
nrt3-jp.ark nrt 6.5 NRT 3.5 pao 101.7 nrt2 3.5
oak5-us.ark lax 19.3 SJC 5.9 sjc0 4.4 pao 4.4
okc-us.ark dfw 9.6 MCI 9.6 dal 8.2 dfw 7.3
ord-us.ark cbf 13.0 ORD 2.9 iad 19.0 chi 1.6
ory4-fr.ark bru 6.5 CDG 2.0 cdg 1.4 cdg1 1.7
ory6-fr.ark bru 6.4 LHR 8.8 lhr 8.4 lon 8.6
ory7-fr.ark bru 7.7 CDG 4.0 cdg 5.0 cdg1 4.0
ory8-fr.ark bru 6.3 CDG 2.0 lhr 25.7 lon 9.4
osl-no.ark lpp 46.5 OSL 0.9 osl 25.8 sto1 9.0
pbh2-bt.ark bom 105.2 MAA 92.0 pbh 1.8 sin 91.7
per-au.ark syd 48.9 PER 2.0 per 1.2 syd 45.3
per2-au.ark syd 140.0 PER 2.5 per 1.5 syd 44.9
phl-us.ark iad 15.9 iad 14.0 rst1 15.9
pna-es.ark mad 33.9 BCN 13.5 bcn 13.3 mad1 19.3
prg-cz.ark fra 16.9 PRG 1.1 qbts1 5.9 prg1 0.6
prg2-cz.ark fra 29.8 PRG 1.7 qfra3 14.0 prg1 1.5
pry-za.ark jnb 167.3 JNB 1.2 jnb 0.6 cpt1 18.4
puw-ru.ark lpp 18.2 DME 1.5 beg 67.1 fra 34.3
pvu-us.ark lax 25.2 SLC 3.8 slc 2.7 sea 18.4
rdu-us.ark iad 9.5 IAD 8.1 iad 7.7 ash 8.6
rdu2-us.ark iad 11.3 IAD 8.1 iad 8.5 ash 8.6
rdu3-us.ark iad 27.5 IAD 18.6 iad 18.4 atl1 23.8
rkv-is.ark lhr 49.0 KEF 2.0 kef 0.8 dub1 23.0
san-us.ark lax 10.4 LAX 4.7 bur 87.2 lax 3.1
san2-us.ark lax 39.0 LAX 6.5 qlax1 7.5 lax 5.6
san4-us.ark lax 26.6 LAX 20.2 bur 106.8 lax 17.3
sao-br.ark gru 117.3 GRU 2.5 qgru1 1.3 rio1 9.6
scq-es.ark mad 31.7 MAD 11.0 mad 10.8 mad1 11.3
sea3-us.ark dls 10.1 SEA 3.9 pao 24.6 sea 3.0
sin-gc.ark sin 4.0 SIN 1.5 qsin1 93.5 sin 1.1
sin-sg.ark sin 3.2 SIN 2.3 qsin1 12.2 sin 0.7
sjc2-us.ark lax 15.2 SJC 1.4 sjc0 0.5 pao 2.1
sjj-ba.ark waw 71.1 BUD 57.5 vie 213.3 mil1 89.4
sjo-cr.ark chs 63.0 SJO 2.2 mia 51.8 mia 78.6
snn-ie.ark lhr 20.4 DUB 4.4 dub 3.9 dub1 3.9
sql-us.ark lax 19.6 SJC 2.2 pao 1.2 pao 0.9
stx-vi.ark chs 38.3 MIA 26.4 mia 25.5 mia 25.1
svo2-ru.ark lpp 22.4 DME 5.8 fra 41.1 fra 38.2
swu-kr.ark hkg 77.6 ICN 6.5 qsin1 76.9 nrt2 38.5
syd3-au.ark syd 4.8 SYD 0.9 syd 1.0 syd 0.5
tij-mx.ark lax 15.8 LAX 6.6 qlax1 6.6 lax 5.6
tlv-il.ark mil 80.2 MRS 43.6 tlv 2.0 mil1 50.5
tlv3-il.ark mil 64.2 TLV 2.1 tlv 2.1 tlv1 3.0
tnr-mg.ark JNB 215.7 jnb 199.1
tpe-tw.ark tpe 10.3 TPE 5.2 tpe 3.1 sin 74.4
vdp-dk.ark lpp 21.9 CPH 5.6 arn 13.4 cph1 5.4
vie-at.ark fra 22.5 FRA 13.8 vie 2.1 prg1 6.5
waw-pl.ark waw 20.1 HAM 31.3 qwaw2 1.3 wrw1 0.9
wbu-us.ark cbf 13.4 DEN 2.7 den 1.8 den1 1.5
wlg2-nz.ark syd 35.3 AKL 17.0 akl2 16.5 syd 32.4
ygk-ca.ark yyz 29.5 YYZ 16.7 iad 35.5 yyz 16.1
yyc-ca.ark dls 26.5 YYC 4.6 sea 21.8 yvr 16.7
zrh-ch.ark zrh 14.5 ZRH 1.1 zrh2 0.6 mil1 8.1
zrh2-ch.ark zrh 16.2 ZRH 1.1 zrh2 1.0 fra 6.8
zrh4-ch.ark zrh 15.8 ZRH 1.5 zrh2 0.8 mil1 9.0
CAIDA has released the 2024-02 Internet Topology Data Kit (ITDK), the 24th ITDK in a series published over the past 14 years. In the year since the 2023-03 release, CAIDA has expanded its Ark platform with both hardware and software vantage points (VPs), and re-architected the ITDK probing software. We have been busy modernizing the software to enable us to collect ITDK snapshots more regularly, as well as annotate the router-level Internet topology graph with more features.
For IPv4, the ITDK probing software is based primarily around two reliable alias resolution techniques. The first, MIDAR, probes for IPID behavior that suggests that responses from different IP addresses had IPID values derived from a single counter, and thus the addresses are assigned to the same router. This inference is challenging because of the sheer number of router addresses observed in macroscopic Internet topologies, and the IPID value is held in a 16-bit field, requiring sophisticated probing techniques to identify distinct counters. The second, iffinder, probes for common source IP addresses in responses to probes sent to different target IP addresses.
In the past few months, we have replaced the MIDAR and iffinder probing component on the Ark VPs to use alias resolution primitives present in scamper (specifically, the midarest, midardisc, and radargun primitives). We used the recently released scamper python module, and 902 lines of python, which executes on a single machine at CAIDA to coordinate the probing from many VPs.
The following table provides statistics illustrating the growth of the ITDK over the past year, driven by the expansion of Ark VPs. Overall, we increased the number of Ark VPs providing topology data from 93 to 142, the number of addresses probed from 2.6 to 3.6M, doubled the number of VPs that we use for alias resolution probing, and found aliases for 50% more addresses than a year ago. Note that we use the term “node” to distinguish between our router inferences, and the actual routers themselves. By definition all routers have at least two IP addresses; our “nodes with at least two IPs” are the subset of routers we were able to observe with that property.
| 2023-03 | 2024-02 | |
| Input: | ||
| Number of addresses probed: | 2.64M | 3.58M |
| Number of ark VPs: | 93 | 142 |
| Number of countries: | 37 | 52 |
| Alias resolution: | ||
| Number of ark VPs for MIDAR: | 55 | 101 |
| Number of ark VPs for iffinder: | 46 | 101 |
| MIDAR + iffinder Output: | ||
| Nodes with at least two IPs: | 75,660 | 107,976 |
| Addresses in nodes with at least two IPs: | 284,479 | 425,964 |
| MIDAR, iffinder, SNMP Output: | ||
| Nodes with at least two IPs: | – | 124,857 |
| Addresses in nodes with at least two IPs: | – | 515,524 |
For 2024-02, we also evaluated the gains provided by SNMPv3 probing, following a paper published in IMC 2021 that showed many routers return a unique SNMP Engine ID in response to a SNMPv3 request; the basic idea is that different IP addresses returning the same SNMPv3 Engine ID are likely aliases. Of the 3.58M addresses we probed, 672K returned an SNMPv3 response. We inferred that IP addresses belonged to the same router when they return the same SNMP Engine ID, the size of the engine ID was at least 4 bytes, the number of engine boots was the same, and the router uptime was the same; we did not use the other filters in section 4.4 of the IMC paper. This inferred 47,770 nodes with at least two IPs, many of which were shared with existing nodes found with MIDAR + iffinder. In total, when we combined MIDAR, iffinder, and SNMP probing, we obtained a graph with 124,857 nodes with at least two IPs, covering 515,524 addresses. We are including both the MIDAR + iffinder and MIDAR + iffinder + SNMP graphs in ITDK 2024-02.
Our ITDK also includes an IPv6 graph derived from speedtrap, which infers that IPv6 addresses belong to the same router if the IPID values in fragmented IPv6 responses appear to be derived from a single counter, and a graph derived from speedtrap and SNMP. For IPv6, the gains provided by SNMP are more significant, as the effectiveness of the IPv6 IPID as an alias inference vector wanes. Of the 929K IPv6 addresses we probed, 68K returned an SNMPv3 response.
| 2023-03 | 2024-02 | |
| Input: | ||
| Number of addresses probed: | 592K | 929K |
| Number of ark VPs: | 36 | 54 |
| Number of countries: | 18 | 25 |
| Speedtrap output: | ||
| Nodes with at least two IPs: | 4,945 | 4,129 |
| Addresses in nodes with at least two IPs: | 12,638 | 10,886 |
| Speedtrap + SNMP output: | ||
| Nodes with at least two IPs: | – | 8,935 |
| Addresses in nodes with at least two IPs: | – | 35,164 |
Beyond the alias resolution, the nodes are also annotated with their bdrmapIT-inferred operator (expressed as an ASN) as well as an inferred geolocation. We look up the PTR records of all router IP addresses with zdns, following CNAMEs where they exist, and provide these names as part of the ITDK. For router geolocation, we used a combination of DNS-based heuristics, IXP geolocation (routers connected to an IXP are likely located at that IXP), and Maxmind GeoLite2.
We inferred DNS-based geolocation heuristics using RTT measurements from 148 Ark VPs in 52 countries to constrain Hoiho, which automatically infers naming conventions in PTR records as regular expressions, and covered 819 different suffixes (e.g., ^.+\.([a-z]+)\d+\.level3\.net$ and ^.+\.([a-z]{3})\d+\.[a-z\d]+\.cogentco\.com$ extract geolocation hints in hostnames for Level3 and Cogent in the above figure). There is no dominant source of geohint observed in these naming conventions; 443 (54.1%) embedded IATA airport codes (e.g. IAD, WAS for the Washington D.C. area), 310 (37.9%) embedded place names (e.g. Ashburn for Ashburn, VA, US), 87 (10.6%) embedded the first six characters of a CLLI code (e.g. ASBNVA for Ashburn), and 12 (1.4%) embedded locodes (e.g. USQAS for Ashburn, VA, US). Interestingly, the operators that used CLLI and locodes had conventions that were more congruent with observed RTT values than operators that used IATA codes or place names. For the nodes in the ITDK, hoiho provided a geolocation inference for 127K, IXP provided a geolocation inference for 14K, and maxmind covered the remainder. The rules we inferred are usable via CAIDA’s Hoiho API.
ITDKs older than one year are publicly available, and ITDK 2024-02 is available to researchers and CAIDA members, after completing a simple form for access.
Acknowledgment: We are grateful to all of the Ark hosting sites, MaxMind’s freely available geolocation database, and academic research access to Iconectiv’s CLLI database to support this work.
The Internet Routing Registry (IRR) is a set of distributed databases used by networks to register routing policy information and to validate messages received in the Border Gateway Protocol (BGP).
First deployed in the 1990s, the IRR remains the most widely used database for routing security purposes, despite the existence of more recent and more secure alternatives such as the Resource Public Key Infrastructure (RPKI). Yet, the IRR lacks a strict validation standard and the limited coordination across different database providers can lead to inaccuracies. Moreover, it has been reported that attackers have begun to register false records in the IRR to bypass operators’ defenses when launching attacks on the Internet routing system, such as BGP hijacks.
In our paper, IRRegularities in the Internet Routing Registry, we at CAIDA/UC San Diego, in collaboration with Georgia Tech and Stanford, proposed a workflow to identify suspicious IRR records. In this post, we succinctly describe how we quantified the inconsistencies across all IRR databases, identified likely suspicious IRR records, and validated our results against relevant studies.
Each IRR database is managed independently under different policies and registration processes. The five RIRs (RIPE, ARIN, APNIC, AFRINIC, and LACNIC) manage authoritative IRR databases. Routing information registered in those IRR databases undergoes a validation process against the address ownership information to ensure correctness. IRR databases operated by other institutions are non-authoritative IRR databases and are not strictly validated.
To increase the likelihood of launching a successful BGP hijack attack, malicious actors may inject false records into non-authoritative IRR databases. There have been reported cases of successful BGP hijacking attempts that also abused the IRR.
In one prominent case, an attacker successfully hijacked Amazon’s address space that was used to host Celer Network’s cryptocurrency exchange website. The attacker, AS209243 (QuickHost.uk), pretended to be an upstream provider of AS16509 (Amazon) by registering false objects in ALTDB. In a different case, AS207427 (GoHosted.eu) registered false IRR objects for 3 UCSD-announced prefixes and hijacked those prefixes in BGP for more than a month.
Following the diagram in Figure 1, we consider an IRR record in a non-authoritative database suspicious if it satisfies the following conditions:
We validate our inferred suspicious IRR records with the Resource Public Key Infrastructure (RPKI), a more recent and secure alternative of the IRR. We also check if the origin ASes in the suspicious IRR records were classified as serial hijacker ASes by Testart et al. published at IMC 2019.
Figure 1. Workflow to identify suspicious IRR records (solid arrows) and methods to validate our results (dotted arrows).
We discuss the results of the first step in the workflow above. To understand the baseline characteristics of the IRR databases, we analyze the consistency between all pairs of IRR databases.
Figure 2 shows the percentage of records with the same prefix but different origin ASes between pairs of IRRs. We found that most IRR databases have mismatching records with one another, consistent with persistent neglect by IRR users and thus an increasing number of outdated entries. We also noticed instances where a company registered records in multiple IRR databases, but only updated the records in one IRR database, causing inter-IRR inconsistency.
Most surprising were the mismatching records between pairs of authoritative IRR databases, since each RIR only allows registration of records containing address blocks managed by that RIR, which do not overlap with each other. We speculate that those mismatching records correspond to address space that was transferred across RIRs, and the address owner from the previous RIR did not remove the outdated object. As of January 2024, two months since our paper was published, the RIRs have removed all inconsistent IRR records in their authoritative databases. We provide the updated results on github https://github.com/CAIDA/IRR-IRRegularities-Analysis
Figure 2. Fraction of inconsistent records in the IRR on the Y-axis with respect to the IRR on the X-axis. The denominators shown in Figure 1b in the paper.
Out of 1.5 million RADB records (1.2 million unique prefixes), we identified 34,199 potentially suspicious records. We further checked the RPKI consistency of those records. Out of those 34,199 records, 4,082 records had a mismatching ASN, 144 had prefixes that were too specific, and 9,450 had no matching ROA in RPKI. To further narrow down the list of suspicious IRR records, we removed the ones whose AS appear in other RPKI-consistent records (assuming those ASes were unlikely to be malicious), leaving 6,373 suspicious records. Network operators who use IRR-based filtering should carefully consider those suspicious records.
We also compared our list of 34,199 suspicious records with the list of serial hijackers from Testart et al. and found 5,581 records registered by 168 serial hijacker ASes. We found one of those ASes to be a small US-based ISP with 10 customers according to CAIDA’s AS Rank. Another serial hijacker AS was a European hosting provider with more than 100 customers, which was also known to be exploited by attackers to abuse the DNS system. However, networks may have registered both suspicious and benign records, which can complicate the inference of suspicious IRR records.
We provided a first look at inconsistencies across IRR databases and proposed an approach to infer suspicious activities in the IRR without external sources of ground truth. We found IRR databases prone to staleness and errors, confirming the importance of operators transitioning to RPKI-based filtering. We hope this work inspires new directions in automating the detection of abuse of IRRs, ideally in time to prevent or thwart an attacker’s ultimate objective. We publicly provide our analysis code on Github https://github.com/CAIDA/IRR-IRRegularities-Analysis with more recent sample data and results.
In the first part of our blog series, we introduced our brand-new python module for scamper, the packet-prober underpinning much of Ark’s ongoing measurements. One aspect that we highlighted was the ability for potential users of Ark to develop their code locally, before running it on the Ark platform. When I develop measurement applications, I use a couple of local Raspberry Pis and my own workstation to get the code correct, and then copy the code to the CAIDA system to run the experiment using available Ark vantage points. The goal of this blog article is to describe different ways to locally develop your measurement experiment code.
Example #1: Starting small with one scamper process.
The easiest way to begin is with one scamper process running on the same system where you develop your python code. With scamper installed (we recommend that you use a package listed on the scamper website), start a scamper process, and make it available for measurement commands on a Unix domain socket. For example, you might run scamper as follows:
$ scamper -U /tmp/scamper -p 100
This will create a Unix domain socket to drive scamper at /tmp/scamper, and tell scamper that it can probe at up to 100 packets/second. You can adjust these parameters to what is appropriate locally.
You can then develop and debug your measurement code in Python. To use this scamper process, your Python code might begin as follows:
01 from scamper import ScamperCtrl
02
03 # use the scamper process available at /tmp/scamper
04 ctrl = ScamperCtrl(unix="/tmp/scamper")
05
06 # do a simple ping to 8.8.8.8 and print the outcome
07 o = ctrl.do_ping("8.8.8.8")
08 if o.min_rtt is not None:
09 print(f"{o.min_rtt.total_seconds()*1000):.1f} ms")
10 else:
11 print("no reply")
Example #2: Coordinating measurements among VPs.
Once you are comfortable using the python module with a single local scamper instance, you might want to test your code with multiple scamper instances, each representing a distinct vantage point. The scamper software includes the sc_remoted interface to support that. sc_remoted has features for authenticating endpoints with TLS, but you might choose to initially operate endpoints without the complexity of TLS.
sc_remoted listens on a port for inbound scamper connections, and makes Unix domain sockets — one for each VP — available in a nominated directory. The best idea is to create an empty directory for these sockets. You might run sc_remoted as follows:
$ mkdir -p /path/to/remote-sockets
$ sc_remoted -U /path/to/remote-sockets -P 50265
The first command creates the directory, because sc_remoted will not create that directory for you. The second command starts sc_remoted listening on port 50265 for incoming scamper connections, and will place Unix domain sockets in /path/to/remote-sockets as they arrive. Note, we use /path/to as a placeholder to the actual path in your local file system that is appropriate for your environment; you might put these sockets in a directory somewhere in your home directory, for example.
Then, on the systems that you want to act as vantage points, the following command:
$ scamper -p 100 -R 192.0.2.28:50265 -M foo.bar
will (1) start a scamper process, (2) tell it that it can probe at up to 100 packets-per-second, (3) connect it to the specified IP address and port to receive measurement commands from, and (4) tell it to identify itself as “foo.bar” to the remote controller. If you go into /path/to/remote-sockets, you might see the following:
$ cd /path/to/remote-sockets
$ ls -l
total 0
srwx------ 1 mjl mjl 0 Jan 22 16:57 foo.bar-192.0.2.120:12369
This socket represents the scamper process you just started. The filename begins with foo.bar, the parameter that you gave to scamper to identify itself. After the dash is the IP address and port number that the remote controller observed the remote system coming from. You can connect as many additional scamper instances as you like, and you will see them listed in the directory individually. You should name each differently with something meaningful to you (foo.bar, bar.baz, etc) so that you can identify them on the system on which you’re writing your python code.
The python code we wrote in Example #1 above might be modified as follows:
01 import sys
02 from scamper import ScamperCtrl
03
04 if len(sys.argv) != 2:
05 print("specify path to unix domain socket")
06 sys.exit(-1)
07
08 # use the remote scamper process available at the specified location
09 ctrl = ScamperCtrl(remote=sys.argv[1])
10
11 # do a simple ping to 8.8.8.8 and print the outcome
12 o = ctrl.do_ping("8.8.8.8")
13 if o.min_rtt is not None:
14 print(f"{o.min_rtt.total_seconds()*1000):.1f} ms")
15 else:
16 print("no reply")
And run as:
$ python ping.py /path/to/remote-sockets/foo.bar-192.0.2.120\:12369
If you have multiple remote-sockets in the directory, you can add them individually, or use all sockets in the directory. For example:
01 import sys
02 from datetime import timedelta
03 from scamper import ScamperCtrl
04
05 if len(sys.argv) != 3:
06 print("usage: single-radius.py $dir $ip")
07 sys.exit(-1)
08
09 ctrl = ScamperCtrl(remote_dir=sys.argv[1])
10 for i in ctrl.instances():
11 ctrl.do_ping(sys.argv[2], inst=i)
12
13 min_rtt = None
14 min_vp = None
15 for o in ctrl.responses(timeout=timedelta(seconds=10)):
16 if o.min_rtt is not None and (min_rtt is None or min_rtt > o.min_rtt):
17 min_rtt = o.min_rtt
18 min_vp = o.inst
19
20 if min_rtt is not None:
21 print(f"{min_vp.name} {(min_rtt.total_seconds()*1000):.1f} ms")
22 else:
23 print(f"no responses for {sys.argv[2]}")
and run this command:
$ python single-radius.py /path/to/remote-sockets 8.8.8.8
We encourage you to reach out via email if you have questions about using the module. In the first instance, you can email ark-info at caida.org.
This is the first in a series of essays about CAIDA’s new effort to reduce the barrier to performing a variety of Internet measurements.
Network operators and researchers often require the ability to conduct active measurements of networks from a specific location in order to understand some property of the network. However, obtaining access to a vantage point at a given location is challenging, as there can be significant trust barriers that prevent access: a platform operator has to provide vantage point hosts with guarantees about the activity that their vantage points will exhibit, and a platform operator has to trust that users will stay within those guarantees. Current access control to active measurement infrastructure has two extremes: access that allows for trusted users to run arbitrary code, and API access that allows arbitrary users to schedule a (limited) set of measurements, and obtain their results. Prior research thrusts in active measurement design have focused on interfaces that allow a user to request a host to send arbitrary packets, leaving the implementation of the measurement to the user. However, this design pattern limits the guarantees that a platform operator can provide a vantage point host regarding what their vantage point will actually do. A domain-specific language for conducting active measurements can alleviate these concerns because it (1) allows a platform operator to specify the measurements that a user can run, and communicate to the host what their vantage point will do, (2) provides users reference implementations of measurement applications that act as building blocks to more complex measurements.
Over the past six months, in consultation with members of the active measurement community, CAIDA has been working towards a next-generation measurement infrastructure, built on the existing Archipelago (Ark) platform. One aspect of this platform is the notion of a researcher development environment that allows for complex, distributed, and reactive measurements built on a well-defined set of measurement primitives. In an effort to make the Ark platform easier to use for measurement researchers, while also providing important access control, CAIDA has developed the scamper python module that provides an interface to the measurement primitives available on each of the Ark nodes. Today, we are releasing the source code of that module, so that researchers can develop and test complex measurements locally, before running vetted experiments on Ark.
The architecture of scamper. Measurement tasks are supplied from one or more input sources, including from an input file, from the command line, or from a control socket.
The module provides user-friendly interfaces to existing scamper functionality. We illustrate the module with some examples.
Example #1: Implementation of RTT-based geolocation measurement
The following is an implementation of the well-known single-radius measurement, which conducts delay measurements to an IP address from a distributed set of vantage points, and reports the shortest of all the RTTs obtained with the name of the monitor, which on Ark, is derived from its location (e.g., lax-us, hlz2-nz, ams-nl, and so on). Researchers and operators might use this technique to understand approximately where a system is located, using the RTT constraint of the vantage point that reports the shortest delay.
01 import sys
02 from datetime import timedelta
03 from scamper import ScamperCtrl
04
05 if len(sys.argv) != 3:
06 print("usage: single-radius.py $dir $ip")
07 sys.exit(-1)
08
09 ctrl = ScamperCtrl(remote_dir=sys.argv[1])
10 for i in ctrl.instances():
11 ctrl.do_ping(sys.argv[2], inst=i)
12
13 min_rtt = None
14 min_vp = None
15 for o in ctrl.responses(timeout=timedelta(seconds=10)):
16 if o.min_rtt is not None and (min_rtt is None or min_rtt > o.min_rtt):
17 min_rtt = o.min_rtt
18 min_vp = o.inst
19
20 if min_rtt is not None:
21 print(f"{min_vp.name} {(min_rtt.total_seconds()*1000):.1f} ms")
22 else:
23 print(f"no responses for {sys.argv[2]}")
This implementation takes two parameters (lines 5-7) — a directory that contains a set of unix domain sockets, each of which represents an interface to a single vantage point, and an IP address to probe. On line 9, we open an interface (represented by a ScamperCtrl object) that contains all of the vantage points in that directory. We then send a ping measurement to each of the vantage point instances (lines 10-11). These ping measurements operate in parallel — the ping measurements on each of the nodes operate asynchronously. We then collect the results of the measurements (lines 13-18), noting the minimum observed RTT, and the vantage point where it came from. We pass a 10-second timeout on line 15, so that a vantage point that experiences an outage after we send the measurements does not hold up the whole experiment. Finally, on lines 20-23, we print the result of the measurement.
Example #2: RTTs to authoritative name servers of a specific domain
The next example shows a more complex measurement. Let’s say we want to know the RTTs to the authoritative name servers for a zone from a single vantage point.
01 import sys
02 from datetime import timedelta
03 from scamper import ScamperCtrl
04
05 if len(sys.argv) != 3:
06 print("usage: authns-delay.py $vp $zone")
07 sys.exit(-1)
08
09 ctrl = ScamperCtrl(remote=sys.argv[1])
10
11 # get the list of NS for the zone
12 o = ctrl.do_dns(sys.argv[2], qtype='NS', wait_timeout=1, sync=True)
13
14 # issue queries for the IP addresses of the authoritative servers
15 ns = {}
16 for rr in o.ans():
17 if rr.ns is not None and rr.ns not in ns:
18 ns[rr.ns] = 1
19 ctrl.do_dns(rr.ns, qtype='A', wait_timeout=1)
20 ctrl.do_dns(rr.ns, qtype='AAAA', wait_timeout=1)
21
22 # collect the unique addresses out of the address lookups
23 addr = {}
24 for o in ctrl.responses(timeout=timedelta(seconds=3)):
25 for a in o.ans_addrs():
26 addr[a] = o.qname
27
28 # collect RTTs for the unique IP addresses
29 for a in addr:
30 ctrl.do_ping(a)
31 for o in ctrl.responses(timeout=timedelta(seconds=10)):
32 print(f"{addr[o.dst]} {o.dst} " +
33 (f"{(o.min_rtt.total_seconds() * 1000):.1f}"
34 if o.min_rtt is not None else "???"))
This implementation takes two parameters (lines 5-7) — a single vantage point, and a zone name to study. As before, we open an interface to that VP on line 9, and then issue a DNS query for the authoritative name servers for the zone (line 12).
There are a couple of interesting things to note about line 12. First, we do not pass a handle representing the VP instance to the do_dns method, as the ScamperCtrl interface only has a single instance associated with it — it is smart enough to use that single instance for the measurement. Second, we pass sync=True to make the measurement synchronous — the method does not return until it has the results of that single measurement. This is shorter (in lines of code) and more readable than issuing an asynchronous measurement and then collecting the single result. Then, we issue asynchronous queries for the IPv4 and IPv6 addresses for the name servers returned (lines 14-20) and send ping measurements for each of the addresses (lines 29-30). Finally, we print the names of the nameservers, their IP addresses, and the minimum RTT observed to each.
The scamper python module supports most of the measurement primitives currently available in scamper: ping, traceroute, DNS query, alias resolution, HTTP, and simple UDP probes. We’ve used the module internally to: (1) reproduce the data collection methodology of a recent router fingerprinting method; (2) study the deployment of Netflix fast.com speed test endpoints, (3) implement MIDAR; (4) study anycast open resolvers, and (5) monitor serial number changes of zones amongst a set of nameservers authoritative for a zone. One important feature of our module is that it provides a python interface, which means that you can use our measurement module alongside existing python modules that parse JSON, etc. The documentation for the module is publicly available and we will write additional blog entries in the coming days that show more of its features, in a digestible form.
In the short term, measurement researchers can request access to the infrastructure to run vetted experiments by emailing ark-info at caida.org. Note: the access does not provide a login on any of the Ark nodes. Rather, it provides access to a system that can access the measurement primitives of the vantage points, as illustrated above. Again, we are releasing the python module to allow researchers to develop and debug experiments locally, before running them on Ark. We hope that this approach provides a convenient development lifecycle for researchers, as the CAIDA system you will have access to when running the experiments will not necessarily have the local development environment that you are accustomed to.
The module itself is written in cython, providing a wrapper around two C libraries in scamper that do much of the heavy lifting. You build and install the module using the instructions on the scamper website, install the module using the Ubuntu PPA (preferred if you are using Ubuntu), or install the module using one of the packages available on other operating systems as these become available.
ACM’s Internet Measurement Conference (IMC) is an annual highly selective venue for the presentation of Internet measurement and analysis research. The average acceptance rate for papers is around 25%. CAIDA researchers co-authored three papers and one poster that was be presented at the IMC conference in Montreal, Quebec on October 26 – 28, 2023. We link to these publications below.
On the Importance of Being an AS: An Approach to Country-Level AS Rankings
Bradley Huffaker, Alexander Marder, Romain Fontugne, kc claffy. ACM Internet Measurement Conference (IMC), 2023.
Recent geopolitical events demonstrate that control of Internet infrastructure in a region is critical to economic activity and defense against armed conflict. This geopolitical importance necessitates novel empirical techniques to assess which countries remain susceptible to degraded or severed Internet connectivity because they rely heavily on networks based in other nation states. Currently, two preeminent BGP-based methods exist to identify influential or market-dominant networks on a global scale-network-level customer cone size and path hegemony–but these metrics fail to capture regional or national differences.
We adapt the two global metrics to capture country-specific differences by restricting the input data for a country-specific metric to destination prefixes in that country. Although conceptually simple, our study required tackling methodological challenges common to most Internet measurement research today, such as geolocation, incomplete data, vantage point access, and lack of ground truth. Restricting public routing data to individual countries requires substantial downsampling compared to global analysis, and we analyze the impact of downsampling on the robustness and stability of our country-specific metrics. As a measure of validation, we apply our country-specific metrics to case studies of Australia, Japan, Russia, Taiwan, and the United States, illuminating aspects of concentration and interdependence in telecommunications markets. To support reproducibility, we will share our code, inferences, and data sets with other researchers.
IRRegularities in the Internet Routing Registry
Ben Du, Gautam Akiwate, Cecilia Testart, Alex C. Snoeren, kc claffy, Katherine Izhikevich, Sumanth Rao. ACM Internet Measurement Conference (IMC), 2023.
The Internet Routing Registry (IRR) is a set of distributed databases used by networks to register routing policy information and to validate messages received in the Border Gateway Protocol (BGP). First deployed in the 1990s, the IRR remains the most widely used database for routing security purposes, despite the existence of more recent and more secure alternatives. Yet, the IRR lacks a strict validation standard and the limited coordination across diferent database providers can lead to inaccuracies. Moreover, it has been reported that attackers have begun to register false records in the IRR to bypass operators’ defenses when launching attacks on the Internet routing system, such as BGP hijacks. In this paper, we provide a longitudinal analysis of the IRR over the span of 1.5 years. We develop a workflow to identify irregular IRR records that contain conflicting information compared to different routing data sources. We identify 34,199 irregular route objects out of 1,542,724 route objects from November 2021 to May 2023 in the largest IRR database and find 6,373 to be potentially suspicious.
Coarse-grained Inference of BGP Community Intent
Thomas Krenc, Alexander Marder, Matthew Luckie, kc claffy. ACM Internet Measurement Conference (IMC), 2023.
BGP communities allow operators to influence routing decisions made by other networks (action communities) and to annotate their network’s routing information with metadata such as where each route was learned or the relationship the network has with their neighbor (information communities). BGP communities also help researchers understand complex Internet routing behaviors. However, there is no standard convention for how operators assign community values, and significant efforts to scalably infer community meanings have ignored this high-level classification. We discovered that doing so comes at a significant cost in accuracy, of both inference and validation. To advance this narrow but powerful direction in Internet infrastructure research, we design and validate an algorithm to execute this first fundamental step: inferring whether a BGP community is action or information. We applied our method to 78,480 community values observed in public BGP data for May 2023. Validating our inferences (24,376 action and 54,104 informational communities) against available ground truth (6,259 communities) we find that our method classified 96.5% correctly. We found that the precision of a state-of-the-art location community inference method increased from 68.2% to 94.8% with our classifications. We publicly share our code, dictionaries, inferences, and datasets to enable the community to benefit from them.
CAIDA also contributed to one extended abstract:
Empirically Testing the PacketLab Model
Tzu-Bin Yan, Zesen Zhang, Bradley Huffaker, Ricky K. P. Mok, kc claffy, Kirill Levchenko. ACM Internet Measurement Conference (IMC) Poster, 2023.
PacketLab is a recently proposed model for accessing remote vantage points. The core design is for the vantage points to export low-level network operations that measurement researchers could rely on to construct more complex measurements. Motivating the model is the assumption that such an approach can overcome persistent challenges such as the operational cost and security concerns of vantage point sharing that researchers face in launching distributed active Internet measurement experiments. However, the limitations imposed by the core design merit a deeper analysis of the applicability of such model to real-world measurements of interest. We undertook this analysis based on a survey of recent Internet measurement studies, followed by an empirical comparison of PacketLab-based versus native implementations of common measurement methods. We showed that for several canonical measurement types common in past studies, PacketLab yielded similar results to native versions of the same measurements. Our results suggest that PacketLab could help reproduce or extend around 16.4% (28 out of 171) of all surveyed studies and accommodate a variety of measurements from latency, throughput, network path, to non-timing data.