Border Gateway Protocol (BGP) datasets with routing records collected from Reseaux IP Europeens (RIPE), BCNET, and Route Views


BGP datasets

Five well-known Border Gateway Anomalies (BGP) anomalies WannaCrypt, Moscow blackout, Slammer, Nimda, Code Red I, occurred in May 2017, May 2005, January 2003, September 2001, and July 2001, respectively. The Reseaux IP Europeens (RIPE) BGP update messages are publicly available from the Network Coordination Centre (NCC) and contain: WannaCrypt, Moscow blackout, Slammer, Nimda, Code Red I, and regular data: https://www.ripe.net/analyse/. Regular data are also collected from BCNET: http://www.bc.net/.
- WannaCrypt (WannaCry) is a cryptoworm ransomware that works by gaining administrative privileges and employs the EternalBlue exploit and DoublePulsar backdoor in systems running Microsoft Windows 7.
- The Chagino substation of the Moscow energy ring experienced a transformer failure on May 24, 2005 at 20:57 (MSK). The event caused a complete shutdown of the substation and a blackout that affected all customer until 16:00 (MSK) of May 26, 2005. During the blackout, the Internet traffic exchange point MSK-IX was disconnected from 11:00 to 17:00 (MSK).
- Slammer infected Microsoft SQL servers through a small piece of code that generated IP addresses at random. The number of infected machines doubled approximately every 9 seconds.
- Nimda exploited vulnerabilities in the Microsoft Internet Information Services (IIS) web servers for Internet Explorer 5. The worm propagated by sending an infected attachment that was automatically downloaded once the email was viewed.
- The Code Red I worm attacked Microsoft IIS web servers by replicating itself through IIS server weaknesses Unlike the Slammer worm, Code Red I searched for vulnerable servers to infect. The rate of infection was doubling every 37 minutes.

37 features are extracted from BGP update messages that originated from AS 513 (route collector rrc 04). The data collected during periods of Internet anomalies include:
- eight-day period for WannaCrypt (four days of the attack as well as two days prior and two days after the attack);
- five-day period for Moscow blackout, Slammer, and Code Red I (the day of the attack as well as two days prior and two days after the attack);
- six-day period for Nimda (two days of the attack as well as two days prior and two days after the attack). Note that there are 31 missing data points in the Nimda dataset.


Raw data from the "route collector rrc 04" are organized in folders labeled by the year and month of the collection date. Complete datasets for WannaCrypt, Moscow blackout, Slammer, Nimda, and Code Red I are available from the RIPE route collector rrc 04 site:
      RIPE NCC: https://www.ripe.net
      Analyze: https://www.ripe.net/analyse
      Internet Measurements: https://www.ripe.net/analyse/internet-measurements
      Routing Information Service (RIS): https://www.ripe.net/analyse/internet-measurements/routing-information-service-ris
      RIS Raw Data: https://www.ripe.net/analyse/internet-measurements/routing-information-service-ris/ris-raw-data      
      rrc04.ripe.net: data.ris.ripe.net/rrc04/
The date of last modification and the size of the datasets are also included.

BGP update messages are originally collected in multi-threaded routing toolkit (MRT) format. "Zebra-dump-parser" written in Perl is used to extract to ASCII the BGP updated messages. The 37 BGP features were extracted using a C# tool to generate uploaded datasets (csv files). Labels have been added based on the periods when data were collected.

File content:
Columns 1-4: time (column 1: hour+minute; column 2: hour; column 3: minute; column 4: second)
Columns 5-41: features
Column 42: labels for the regular (-1) and anomalous (1) data.
Note that for RNN algorithms, the PyTorch library requires that label (-1) be changed to (0).

List of features extracted from BGP update messages:
1. Number of announcements
2. Number of withdrawals
3. Number of announced NLRI prefixes
4. Number of withdrawn NLRI prefixes
5. Average AS-path length
6. Maximum AS-path length
7. Average unique AS-path length
8. Number of duplicate announcements
9. Number of duplicate withdrawals
10. Number of implicit withdrawals
11. Average edit distance
12. Maximum edit distance
13. Inter-arrival time
14-24. Maximum edit distance = n, n = 7, ..., 17
25-33. Maximum AS-path length = n, n = 7, ..., 15
34. Number of Interior Gateway Protocol (IGP) packets
35. Number of Exterior Gateway Protocol (EGP) packets
36. Number of incomplete packets
37. Packet size (B)


Tools

  • Zebra-dump-parser tool
  • C# code
  • Other tools/libraries for analyzing BGP data

    Download datasets

    RIPE

  • BGP RIPE csv datasets
  • BGP RIPE Matlab datasets
  • BGP RIPE Matlab datasets with concatenations

    Route Views

  • BGP Route Views csv datasets
  • BGP Route Views Matlab datasets

    Publications

  • Z. Li, A. L. Gonzalez Rios, and Lj. Trajkovic, "Detecting Internet worms, ransomware, and blackouts using recurrent neural networks," in Proc. IEEE International Conference on Systems, Man, and Cybernetics," Toronto, Canada, Oct. 2020, pp. 2165-2172.
  • Z. Li, A. L. Gonzalez Rios, G. Xu, and Lj. Trajkovic, "Machine learning techniques for classifying network anomalies and intrusions," in Proc. IEEE Int. Symp. Circuits and Systems, Sapporo, Japan, May 2019, pp. 1-5.
  • A. L. Gonzalez Rios, Z. Li, G. Xu, A. Diaz Alonso, and Lj. Trajkovic, "Detecting network anomalies and intrusions in communication networks," in Proc. 23rd IEEE International Conference on Intelligent Engineering Systems 2019, Godollo, Hungary, April 2019, pp. 29-34.
  • Qingye Ding's M.A.Sc. thesis: "Application of machine learning techniques for detecting anomalies in communication networks" and presentation slides, June 2018.
  • Book chapter: Q. Ding, Z. Li, S. Haeri, and Lj. Trajkovic, "Application of machine learning techniques to detecting anomalies in communication networks: datasets and feature selection algorithms," in Cyber Threat Intelligence, M. Conti, A. Dehghantanha, and T. Dargahi, Eds., Berlin: Springer, pp. 47-70, 2018.
  • Book chapter: Z. Li, Q. Ding, S. Haeri, and Lj. Trajkovic, "Application of machine learning techniques to detecting anomalies in communication networks: classification algorithms," in Cyber Threat Intelligence, M. Conti, A. Dehghantanha, and T. Dargahi, Eds., Berlin: Springer, pp. 71-92, 2018.
  • Paper: P. Batta, M. Singh, Z. Li, Q. Ding, and Lj. Trajkovic, "Evaluation of support vector machine kernels for detecting network anomalies," IEEE Int. Symp. Circuits and Systems, Florence, Italy, May 2018, pp. 1-4.
  • Presentation: P. Batta, M. Singh, Z. Li, Q. Ding, and Lj. Trajkovic, "Evaluation of support vector machine kernels for detecting network anomalies," Proc. IEEE Int. Symp. Circuits and Systems, Florence, Italy, May 2018, pp. 1-4.
  • Publication: Q. Ding, Z. Li, P. Batta, and Lj. Trajkovic, "Detecting BGP anomalies using machine learning techniques," in Proc. IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016), Budapest, Hungary, Oct. 2016, pp. 3352-3355.
  • Poster: Q. Ding, Z. Li, P. Batta, and Lj. Trajkovic, "Detecting BGP anomalies using machine learning techniques," in Proc. IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016), Budapest, Hungary, Oct. 2016, pp. 3352-3355.

  • Questions: Please contact Zhida Li at <zhidal at sfu.ca>.