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Auto-Recon: An Automated Network Reconnaissance System Based on Knowledge Graph

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Algorithms and Architectures for Parallel Processing (ICA3PP 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 13157))

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Abstract

Effective network security management is usually based on comprehensive, accurate and real-time control of enterprise network. With the rapid growth of enterprise network information, their exposure on the Internet is also expanding rapidly, which raises many security issues. It is hard for the existing approaches of network security management to cover dynamic and hidden assets. Moreover, black-box-based network reconnaissance is highly dependent on expert experience and time-consuming, which cannot meet the needs of enterprises to perform testing periodically. Therefore, target-oriented automated reconnaissance of network information becomes an urgent problem to be solved.

We proposed and constructed a knowledge graph based network reconnaissance model, NRG, from a method-level perspective which describes the relationship between different network information and the way to reconnaissance them. Based on NRG, we have designed and implemented Auto-Recon, an automated network reconnaissance system using the distributed architecture. The purpose of Auto-Recon is to automatically find exposed surfaces of targets on the Internet using the primary domain as initial information. The system reduces strong dependence on network reconnaissance knowledge and experience. We conducted an experiment and the result shows that Auto-Recon has better performance in terms of efficiency, effectiveness and automation than existing tools.

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References

  1. TeamfromKnownsec: Zoomeyesearchengine. https://www.zoomeye.org

  2. ARL: ARL. https://github.com/TophantTechnology/ARL

  3. Arnaert, M., Bertrand, Y., Boudaoud, K.: Modeling vulnerable internet of things on SHODAN and CENSYS: an ontology for cyber security. In: Proceedings of the Tenth International Conference on Emerging Security Information, Systems and Technologies (SECUREWARE 2016), pp. 299–302 (2016)

    Google Scholar 

  4. Bhuyan, M.H., Bhattacharyya, D.K., Kalita, J.K.: Surveying port scans and their detection methodologies. Comput. J. 54(10), 1565–1581 (2011)

    Article  Google Scholar 

  5. CNCERT: 2020 CNCERT cybersecurity analysis (2021). https://www.cert.org.cn/publish/main/upload/File/2020 CNCERT Cybersecurity Analysis.pdf

  6. IANA: Service name and transport protocol port number registry. https://www.iana.org/assignments/service-names-port-numbers/service-names-port-numbers.xhtml

  7. Kang, H.-J., Kim, M.-S., Hong, J.W.-K.: A method on multimedia service traffic monitoring and analysis. In: Brunner, M., Keller, A. (eds.) DSOM 2003. LNCS, vol. 2867, pp. 93–105. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-39671-0_9

    Chapter  Google Scholar 

  8. Kim, M.S., Won, Y.J., Hong, J.W.K.: Application-level traffic monitoring and an analysis on IP networks. ETRI J. 27(1), 22–42 (2005)

    Article  Google Scholar 

  9. Kowta, A.S.L., Bhowmick, K., Kaur, J.R., Jeyanthi, N.: Analysis and overview of information gathering & tools for pentesting. In: 2021 International Conference on Computer Communication and Informatics (ICCCI), pp. 1–13. IEEE (2021)

    Google Scholar 

  10. Lee, D., Rowe, J., Ko, C., Levitt, K.: Detecting and defending against web-server fingerprinting. In: Proceedings of the 18th Annual Computer Security Applications Conference, pp. 321–330. IEEE (2002)

    Google Scholar 

  11. Li, R., Shen, M., Yu, H., Li, C., Duan, P., Zhu, L.: A survey on cyberspace search engines. In: Lu, W., et al. (eds.) CNCERT 2020. CCIS, vol. 1299, pp. 206–214. Springer, Singapore (2020). https://doi.org/10.1007/978-981-33-4922-3_15

    Chapter  Google Scholar 

  12. Matherly, J.: Complete guide to shodan. Shodan LLC (2016-02-25), vol. 1 (2015)

    Google Scholar 

  13. Mirjalili, M., Nowroozi, A., Alidoosti, M.: A survey on web penetration test. Int. J. Adv. Comput. Sci. 3 (2014)

    Google Scholar 

  14. Moore, A.W., Papagiannaki, K.: Toward the accurate identification of network applications. In: Dovrolis, C. (ed.) PAM 2005. LNCS, vol. 3431, pp. 41–54. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-31966-5_4

    Chapter  Google Scholar 

  15. Neo4j: Neo4j. https://neo4j.com/download/

  16. Nmap: Nmap. https://nmap.org/

  17. nmap: Port selection data and strategies. https://nmap.org/book/performance-port-selection.html

  18. OneForAll: Oneforall. https://github.com/shmilylty/OneForAll

  19. Rate-limiting queries: Rate-limiting queries. https://developers.google.com/speed/public-dns/docs/security#rate_limit

  20. Radhakrishnan, S.V.: A framework for system fingerprinting. Ph.D. thesis, Georgia Institute of Technology (2013)

    Google Scholar 

  21. Ramadhan, R.A., Aresta, R.M., Hariyadi, D.: Sudomy: information gathering tools for subdomain enumeration and analysis. IOP Conf. Ser. Mater. Sci. Eng. 771, 012019 (2020). https://doi.org/10.1088/1757-899x/771/1/012019

  22. reNgine: rengine. https://github.com/yogeshojha/rengine

  23. Roughan, M., Sen, S., Spatscheck, O., Duffield, N.: Class-of-service mapping for QOS: a statistical signature-based approach to IP traffic classification. In: Proceedings of the 4th ACM SIGCOMM Conference on Internet Measurement, pp. 135–148 (2004)

    Google Scholar 

  24. Sen, S., Spatscheck, O., Wang, D.: Accurate, scalable in-network identification of P2P traffic using application signatures. In: Proceedings of the 13th International Conference on World Wide Web, pp. 512–521 (2004)

    Google Scholar 

  25. Shivayogimath, C.N.: An overview of network penetration testing. Int. J. Res. Eng. Technol. 3(07), 5 (2014)

    Google Scholar 

  26. subfinder: subfinder. https://github.com/projectdiscovery/subfinder

  27. Sublist3r: Sublist3r. https://github.com/aboul3la/Sublist3r

  28. Trowbridge, C.: An overview of remote operating system fingerprinting. SANS InfoSec Reading Room-Penetration Testing (2003)

    Google Scholar 

  29. Van Der Merwe, J., Caceres, R., Chu, Y.h., Sreenan, C.: Mmdump: a tool for monitoring internet multimedia traffic. ACM SIGCOMM Comput. Commun. Rev. 30(5), 48–59 (2000)

    Google Scholar 

  30. Yeo, J.: Using penetration testing to enhance your company’s security. Comput. Fraud Secur. 2013(4), 17–20 (2013)

    Article  Google Scholar 

  31. Yerrapragada, K.P.: Google hacking !! (2007). Accessed 20 Jan 2010

    Google Scholar 

  32. zmap: zmap. https://github.com/zmap/zmap

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Acknowledgement

We thank anonymous reviewers for their invaluable comments and suggestions. This research was supported in part by Key Laboratory of Network Assessment Technology (Chinese Academy of Science) and Beijing Key Laboratory of Network Security and Protection Technology.

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Authors and Affiliations

  1. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Xiang Zhang, Qingli Guo, Yuan Liu & Xiaorui Gong

  2. School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China

    Xiang Zhang & Xiaorui Gong

Authors
  1. Xiang Zhang
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  2. Qingli Guo
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  3. Yuan Liu
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  4. Xiaorui Gong
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Corresponding author

Correspondence to Qingli Guo .

Editor information

Editors and Affiliations

  1. Xiamen University, Xiamen, China

    Yongxuan Lai

  2. Beijing Normal University, Zhuhai, China

    Tian Wang

  3. Xiamen University, Xiamen, China

    Min Jiang

  4. Tianjin University, Tianjin, China

    Guangquan Xu

  5. Hunan University, Changsha, China

    Wei Liang

  6. University of Naples Parthenope, Naples, Italy

    Aniello Castiglione

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Zhang, X., Guo, Q., Liu, Y., Gong, X. (2022). Auto-Recon: An Automated Network Reconnaissance System Based on Knowledge Graph. In: Lai, Y., Wang, T., Jiang, M., Xu, G., Liang, W., Castiglione, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2021. Lecture Notes in Computer Science(), vol 13157. Springer, Cham. https://doi.org/10.1007/978-3-030-95391-1_7

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  • DOI: https://doi.org/10.1007/978-3-030-95391-1_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-95390-4

  • Online ISBN: 978-3-030-95391-1

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