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Web-Based Intelligence for IDS

  • Christopher B. Freas
  • Robert W. HarrisonEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11637)

Abstract

We and others have shown that machine learning can detect and mitigate web-based attacks and the propagation of malware. High performance machine learning frameworks exist for the major computer languages used to program both web servers and web pages. This paper examines the factors required to use the frameworks as an effective distributed deterrent.

Keywords

Networks Attack detection Machine learning Application level intelligence Security 

References

  1. 1.
    ACM SIGKDD: KDD Cup 1999: Computer network intrusion detection. http://www.kdd.org/kdd-cup/view/kdd-cup-1999/Data
  2. 2.
    Barth, A., Jackson, C., Mitchell, J.C.: Robust defenses for cross-site request forgery. In: Proceedings of the 15th ACM Conference on Computer and Communications Security, pp. 75–88. ACM (2008)Google Scholar
  3. 3.
    ClamavNet: ClamAV is an open source antivirus engine for detecting trojans, viruses, malware & other malicious threats. https://www.clamav.net/. Accessed 26 May 2019
  4. 4.
    Freas, C.B., Harrison, R.W., Long, Y.: High performance attack estimation in large-scale network flows. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5014–5020. IEEE (2018)Google Scholar
  5. 5.
    Google: Tensorflow for Javascript. https://www.tensorflow.org/js. Accessed 26 May 2019
  6. 6.
    Maymounkov, P., Mazières, D.: Kademlia: a peer-to-peer information system based on the XOR metric. In: Druschel, P., Kaashoek, F., Rowstron, A. (eds.) IPTPS 2002. LNCS, vol. 2429, pp. 53–65. Springer, Heidelberg (2002).  https://doi.org/10.1007/3-540-45748-8_5CrossRefzbMATHGoogle Scholar
  7. 7.
    Miller, S., Curran, K., Lunney, T.: Cloud-based machine learning for the detection of anonymous web proxies. In: 2016 27th Irish Signals and Systems Conference (ISSC), pp. 1–6. IEEE (2016)Google Scholar
  8. 8.
    Muscat, I.: What is cross-site request forgery? June 2017. https://www.acunetix.com/blog/articles/cross-site-request-forgery/
  9. 9.
    Oehlert, P.: Violating assumptions with fuzzing. IEEE Secur. Priv. 3(2), 58–62 (2005)CrossRefGoogle Scholar
  10. 10.
    Scholte, T., Robertson, W., Balzarotti, D., Kirda, E.: Preventing input validation vulnerabilities in web applications through automated type analysis. In: 2012 IEEE 36th Annual Computer Software and Applications Conference, pp. 233–243. IEEE (2012)Google Scholar
  11. 11.
    Sharafaldin, I., Lashkari, A.H., Ghorbani, A.A.: Toward generating a new intrusion detection dataset and intrusion traffic characterization. In: Proceedings of Fourth International Conference on Information Systems Security and Privacy, ICISSP (2018)Google Scholar
  12. 12.
    Stoica, I., Morris, R., Karger, D., Kaashoek, M.F., Balakrishnan, H.: Chord: a scalable peer-to-peer lookup service for internet applications. ACM SIGCOMM Comput. Commun. Rev. 31(4), 149–160 (2001)CrossRefGoogle Scholar
  13. 13.
    Syronex: Why is Form Validation Needed? https://formsmarts.com/form-validation
  14. 14.
    Xu, W., Bhatkar, S., Sekar, R.: Practical dynamic taint analysis for countering input validation attacks on web applications. Technical report SECLAB-05-04, Department of Computer Science (2005)Google Scholar
  15. 15.
    Zasso, M.: Machine learning and numerical analysis tools in Javascript for node.js and the browser. https://github.com/mljs. Accessed 26 May 2019
  16. 16.
    Zomlot, L., Chandran, S., Caragea, D., Ou, X.: Aiding intrusion analysis using machine learning. In: 2013 12th International Conference on Machine Learning and Applications, vol. 2, pp. 40–47, December 2013.  https://doi.org/10.1109/ICMLA.2013.103

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceGeorgia State UniversityAtlantaUSA

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