Advertisement

On the potential applications of data mining for information security provision of cloud-based environments

  • A. A. Grusho
  • M. I. Zabezhailo
  • A. A. Zatsarinnyi
  • V. O. Piskovskii
  • S. V. Borokhov
Article

Abstract

An overview of several applications of techniques and models of data mining (DM) in applied information security systems is presented. Special focus is put on the new and actively developed area of cloud-based computing environments. Both the available and future applicabilities of models and techniques of artificial intelligence to IS problem solving are discussed.

Keywords

data mining cloud-based computing information security mathematical models and techniques 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Information Security Doctrine of the Russian Federation (approved by the President of the Russian Federation on September 9, 2000, N Pr-1895). http://www.scrf.gov.ru/documents/5.htmlGoogle Scholar
  2. 2.
    Voronina, Yu., Ross. Gaz., 2015, no. 984. http://www.rg.ru/printable/2015/02/10/ib.htmlGoogle Scholar
  3. 3.
    Zakharov, V.A., Smelyanskii, R.L., and Chemeritskii, E.V., The formal model and verification problems of software-configurable networks, Model. Anal. Inf. Sist., 2013, vol. 20, no. 6, pp. 33–48.Google Scholar
  4. 4.
    Zakharov, V.A. and Chemeritskii, E.V., Some problems of reconfiguration of software-configurable networks, Model. Anal. Inf. Sist., 2014, vol. 21, no. 6, pp. 57–70.Google Scholar
  5. 5.
    GOST (State Standard) R ISO/IEC 15408-1-2008: Information Technology. Methods and Means of Ensuring Safety. Criteria for Information Technology Security Evaluation, 2008.Google Scholar
  6. 6.
    Ashby, R.W., An Introduction to Cybernetics, London: Chapman & Hall, 1956. http://pcp.vub.ac.be/ASHB-BOOK.htmlCrossRefzbMATHGoogle Scholar
  7. 7.
    Denning, D.E., An intrusion detection model, Proceedings of the Seventh IEEE Symposium on Security and Privacy, 1986, pp. 119–131.Google Scholar
  8. 8.
    Lunt, T.F., Detecting intruders in computer systems, Proceedings of the 1993 conference on auditing and computer technology. http://www.researchgate.net/profile/Teresa_Lunt/publication/2304057_Detecting_Intruders_in_Computer_Systems/links/552e86500cf2acd38cba5c94.pdfGoogle Scholar
  9. 9.
    Anderson, D., Lunt, T.F., Javitz, H., Tamaru, A., and Valdes, A., Detecting Unusual Program Behavior Using the Statistical Component of the Next-Generation Intrusion Detection Expert System (NIDES), SRI International, Computer Science Laboratory. http://www.csl.sri.com/papers/5sri/5sri.pdfGoogle Scholar
  10. 10.
    Vaccaro, H.S. and Liepins, G.E., Detection of anomalous computer session activity, The 1989 IEEE Symposium on Security and Privacy, Oakland, CA, 1989, pp. 280–289.CrossRefGoogle Scholar
  11. 11.
    Teng, H.S., Chen, K., and Lu, S.C-Y., Adaptive realtime anomaly detection using inductively generated sequential patterns, IEEE Symposium on Security and Privacy, 1990, pp. 278–284.Google Scholar
  12. 12.
    Catalog of Means for Information Protection. http://zlonov.ru/catalog/Google Scholar
  13. 13.
    Drozd, A., Review of corporate IPS-solutions in the Russian market. http://www.anti-malware.ru/IPS_russian_market_review_2013Google Scholar
  14. 14.
    Intrusion Prevention Systems. Moxize: IT Solution Discovery & Research. http://www.moxize.com/Category/Detail/20/intrusion-prevention-systemsGoogle Scholar
  15. 15.
    USENIX Security’14 (23-th USENIX Security Symposium), San Diego, CA, 2014. http://www.usenix.org/conference/usenixsecurity14Google Scholar
  16. 16.
    IEEE Symposium on Security and Privacy, San Jose, CA, 2014. http://www.ieee-security.org/TC/SP2014/index.htmlGoogle Scholar
  17. 17.
    Financial Action Task Force13. http://www.fatf-gafi.org/Google Scholar
  18. 18.
    VISA: Fraud Prevention Tools & Real Time Fraud Detection. http://usa.visa.com/personal/security/security-program/index.jspGoogle Scholar
  19. 19.
    FORTUNE: 100 Best Companies to Work for. SAS Institute. http://fortune.com/best-companies/sas-institute-4/Google Scholar
  20. 20.
    SAS Institute (Inc.). Patent Applications. http://www.faqs.org/patents/assignee/sas-institute-inc/Google Scholar
  21. 21.
    Intel Security. http://www.intelsecurity.com/Google Scholar
  22. 22.
    Clark, D., Intel Lead $100 Million Investment into Mirantis, The Wall Street Journal, Aug. 24, 2015. http://www.wsj.com/articles/intel-to-lead-100-millioninvestment-into-mirantis-1440388913Google Scholar
  23. 23.
    Zhu, W.-D., Foyle, B., Gagné, D., Gupta, V., Magdalen, J., Mund I, A.S., Nasukawa, T., Paulis, M., Singer, J., and Triska, M., IBM Watson Content Analytics: Discovering Actionable Insight from Your Content, IBM Redbooks: IBM Corp., 2014, 3rd ed. http://www.redbooks.ibm.com/abstracts/sg247877.html?OpenGoogle Scholar
  24. 24.
    Bagchi, S., Barborak, M.A., Buchanan, D.W., ChuCarroll, J., Ferrucci, D.A., Glass, M.R., Kalyanpur, A., Mueller, E.T., Murdock, J.W., Patwardhan, S., Prager, J.M., and Welty, C.A., WatsonPaths: ScenarioBased Question Answering and Inference over Unstructured Information (IBM Research Report RC25489), Yorktown Heights, NY: IBM Thomas J. Watson Research Center, 2014. http://www.patwardhans.net/papers/LallyEtAl14.pdfGoogle Scholar
  25. 25.
    BMC, Remedy. http://www.bmc.com/it-solutions/remedy-itsm.htmlGoogle Scholar
  26. 26.
    BMC Software, Eucalyptus, HP, IBM, Intel, Red Hat and SUSE Create Open Virtualization Alliance. https://openvirtualizationalliance.org/news-events/news/2011/05/bmc-software-eucalyptus-hp-ibm-intel-red-hatand-suse-create-openGoogle Scholar
  27. 27.
    HP Open View. Enterprise Security. http://www8.hp.com/us/en/software-solutions/enterprise-security.htmlGoogle Scholar
  28. 28.
    Carasso, D., Splunk, CITO Research, 2013.Google Scholar
  29. 29.
    Carasso, D., Data Mining with Splunk. http://www.slideshare.net/davidcarasso/datamining5Google Scholar
  30. 30.
    Cohen, P., Big Mechanism (DARPA Big Mechanism Program). http://www.darpa.mil/program/big-mechanismGoogle Scholar
  31. 31.
    Data Mining Using SAS Enterprise Miner. A Case Study Approach. http://support.sas.com/documentation/cdl/en/emcs/66392/PDF/default/emcs.pdfGoogle Scholar
  32. 32.
    SAS/STAT 14.1. User’s Guide. High-Performance Procedures. http://support.sas.com/documentation/cdl/en/stathpug/68163/PDF/default/stathpug.pdfGoogle Scholar
  33. 33.
    Pearl, J., Causality: Models, Reasoning, and Inference, Cambridge: Cambridge University Press, 2000.Google Scholar
  34. 34.
    Agrawal, R., Imielinski, T., and Swami, A., Mining association rules between sets of items in large databases, Proc. 1993 ACM SIGMOD International Conference on Management of Data (SIGMOD'93), New York, 1993, pp. 207–216.CrossRefGoogle Scholar
  35. 35.
    Agrawal, R. and Srikant, R., Fast algorithms for mining association rules, Proc. 20th Int. Conf. Very Large Data Bases (VLDB), Morgan Kaufmann, 1994, pp. 487–499.Google Scholar
  36. 36.
    Tkach, D., Text Mining Technology: Turning Information into Knowledge, IBM White Paper, 1998. http://www.math.unipd.it/~dulli/corso04/whiteweb.pdfGoogle Scholar
  37. 37.
    Plotkin, G.D., A note on inductive generalization, Mach. Intell., 1970, no. 5, pp. 153–164.MathSciNetGoogle Scholar
  38. 38.
    Plotkin, G.D., A further note on inductive generalization, Mach. Intell., 1971, no. 6, pp. 101–124.MathSciNetzbMATHGoogle Scholar
  39. 39.
    Kazemian, P., Chang, M., Zeng, H., Varghese, G., McKeown, N., and Whyte, S., Real time network policy checking using header space analysis, Proc. 10th USENIX Symposium on Networked Systems Design and Implementation,, Chicago, IL, 2013, pp. 99–111. www.usenix.org/system/files/conference/nsdi13/nsdi13final8.pdfGoogle Scholar
  40. 40.
    Kazemian, P., Varghese, G., and McKeown, N., Header space analysis: Static checking for networks, Proc. 9th USENIX Symposium on Networked Systems Design and Implementation, San Jose, CA, 2012, pp. 49–54. http://yuba.stanford.edu/~peyman/docs/headerspace-nsdi12.pdfGoogle Scholar
  41. 41.
    Snort. http://www.snort.orgGoogle Scholar
  42. 42.
    Database of signatures of system Snort. http://www.snort.org/snortrules/Google Scholar
  43. 43.
    Galatenko, A.V., Automaton models of protected computer systems, Intell. Sist., vol. 11, no. 1–4, pp. 403–418.Google Scholar
  44. 44.
    Aleksandrov, D.E., Effective methods for checking the content of network packets by regular expressions, Intell. Sist., 2014, vol. 18, no. 1, pp. 37–60.Google Scholar
  45. 45.
    Zhuravlev, Yu.I., Correct algebras on sets of incorrect (heuristic) algorithms, Kibernetika, Part I, 1977, no. 4, pp. 5–17; Part II, 1977, no. 6, pp. 21–27; Part III, 1978, no. 2, pp. 35–43.Google Scholar
  46. 46.
    Zhuravlev, Yu.I., Ryazanov, V.V., and Sen’ko, O.V., “Raspoznavanie”. Matematicheskie metody. Programmnaya sistema. Prakticheskie primeneniya (“Recognition.” Mathematical Methods. Software System. Practical Applications), Moscow: Fazis, 2006.Google Scholar
  47. 47.
    Rudakov, K.V., Some universal restrictions for classification algorithms, Zh. Vychisl. Mat. Mat. Fiz., 1986, vol. 26, no. 11, pp. 1719–1730.MathSciNetzbMATHGoogle Scholar
  48. 48.
    Avtomaticheskoe porozhdenie gipotez v intellektual’nykh sistemakh (Automatic Generation of Hypotheses in Intelligent Systems), Finn, V.K., Ed., Moscow: Librokom, 2009.Google Scholar
  49. 49.
    Finn, V.K., J.S. Mill’s inductive methods in artificial intelligence systems, Sci. Tech. Inf. Process., Part I, 2011, vol. 38, no. 6, pp. 385–402; Part II, 2012, vol. 39, no. 5, pp. 241–260.CrossRefGoogle Scholar
  50. 50.
    Zabezhailo, M.I., Some capabilities of enumeration control in the DSM method, Sci. Tech. Inf. Process., Part I, 2014, vol. 41, no. 6, pp. 335–347; Part II, 2014, vol. 41, no. 6, pp. 348–361.CrossRefGoogle Scholar
  51. 51.
    Base SAS. High-Performance Procedures. http://support.sas.com/documentation/cdl/en/prochp/68141/PDF/default/prochp.pdfGoogle Scholar
  52. 52.
    IBM Cloud Services. http://www-935.ibm.com/services/us/en/it-services/cloud-services/Google Scholar
  53. 53.
    GENI: Exploring Networks of the Future. http://www.geni.netGoogle Scholar
  54. 54.
    HP TippingPoint. http://www8.hp.com/ru/ru/softwaresolutions/network-security/index.htmlGoogle Scholar
  55. 55.
    Cisco Cloud Security White Papers. http://www.cisco.com/c/en/us/products/security/cloudweb-security/white-paper-listing.htmlGoogle Scholar
  56. 56.
    Intel DPDK: Data Plane Development Kit. http://dpdk.org/Google Scholar
  57. 57.
    ADI QuickStart SDN Development Kit (SDK). http://www.sdxcentral.com/products/adi-engineeringgigabit-sdn-quickstart-development-kit/Google Scholar
  58. 58.
    Intel launches SDN platform Seacliff Trail. http://servernews.ru/tags/sdn-платформаGoogle Scholar

Copyright information

© Allerton Press, Inc. 2015

Authors and Affiliations

  1. 1.Federal Research Center Computer Science and ControlRussian Academy of SciencesMoscowRussia
  2. 2.All-Russian Institute for Scientific and Technical InformationRussian Academy of SciencesMoscowRussia
  3. 3.Applied Research Center for Computer SolutionsMoscowRussia

Personalised recommendations