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Role of Soft Outlier Analysis in Database Intrusion Detection

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Advanced Computing and Intelligent Engineering

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1082))

Abstract

With the rapid development of World Wide Web and E-commerce, concern of security is a very sensitive issue in this modern era of information and communication technology. A lot of financial and brain effort has been invested in this problem and still requires serious attention due to the increasing threats. Database centered Intrusion Detection is a prominent field in this research circumference. Concept of outlier analysis in data mining can automate this intrusion detection process with higher accuracy. In this research, we present the role of soft outlier analysis in Database-centered Intrusion Detection while comparing its performance with its counterpart hard outlier analysis which ultimately enhances its productivity by improving the accuracy and reducing the false positive costs.

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References

  1. Mathew, S., Petropoulos, M., Ngo, H.Q., Upadhyaya, S.: A data-centric approach to insider attack detection in database systems. LNCS 6307, 382–401 (2010)

    Google Scholar 

  2. Abadeh, M.S., Habibi, J., Lucas, C.: Intrusion detection using a fuzzy genetics-based learning algorithm. J. Netw. Comput. Appl. 30, 414–428 (2007)

    Article  Google Scholar 

  3. Christina, Y.C., Michael, G., Karl, L.: DEMIDS: a misuse detection system for database systems. In: Proceedings of the Third Annual IFIP TC-11 WG 11.5 Working Conference on Integrity and Internal Control in Information Systems, pp. 158–178. Amsterdam, Netherlands (1999)

    Google Scholar 

  4. Lee, S.Y., Low, W.L., Wong, P. R.: Learning fingerprints for a database intrusion detection system. In: ESORICS 2002. Lecture Notes in Computer Science, No. 2502, pp. 264–280. Springer (2002)

    Google Scholar 

  5. Christina, Y.C., Michael, G., Karl, L.: Demids: a misuse detection system for database systems. In: Third International IFIP TC-11 WG11.5 Working Conference on Integrity and Internal Control in Information Systems, pp. 159–178 (1999)

    Google Scholar 

  6. Cho, S.: Incorporating soft computing techniques into a probabilistic intrusion detection system. IEEE Trans. Syst., Man Cybern.-Part C Appl. Rev. 32, 154–160 (2002)

    Article  Google Scholar 

  7. Panigrahi, S., Sural, S.: Detection of database intrusion using two-stage fuzzy system. LNCS 5735, 107–120 (2009)

    MATH  Google Scholar 

  8. Zhong, Y., Zhu, Z., Qin, X.: A clustering method based on data queries and its application in database intrusion detection. In: Proceedings of The Fourth International Conference on Machine Learning and Cybernetics, pp. 2096–2101 (2005)

    Google Scholar 

  9. Khan, M.I., Sullivan, B., Foley, S.N.: A semantic approach to frequency based anomaly detection of insider access in database management system. In: CRiSIS 2017, LNCS 10694, pp. 18–28 (2018)

    Google Scholar 

  10. Kim, S., Cho, N.W., Lee, Y.J., Kang, S., Kim, T., Hwang, H., Mun, D.: Application of density-based outlier detection to database activity monitoring. Inf. Syst. Front. 15, 55–65 (2013)

    Article  Google Scholar 

  11. Panigrahi, S., Sural, S., Majumdar, A.K.: Two-stage database intrusion detection by combining multiple evidence and belief update. Inf. Syst. Front. (2010)

    Google Scholar 

  12. Breunig, M.M., Kriegel, H., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers. In: Proceedings of the ACM Sigmod, International Conference on Management of Data Dalles TX (2000)

    Google Scholar 

  13. Zhu, L., Chung, F., Wang, S.: Generalized C-means clustering algorithm with improved fuzzy partitions. IEEE Syst. Man Cybern. Soc. 39(3), 578–591 (2009)

    Article  Google Scholar 

  14. Jang J.S.R.: Neuro-fuzzy and soft computing, 1st edn. PHI/Pearson Education, New Delhi, (2004)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science & Engineering, VSSUT, Burla, India

    Anitarani Brahma & Suvasini Panigrahi

Authors
  1. Anitarani Brahma
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  2. Suvasini Panigrahi
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Corresponding author

Correspondence to Anitarani Brahma .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Rama Devi Women’s University, Bhubaneswar, Odisha, India

    Bibudhendu Pati

  2. Department of Computer Science, Rama Devi Women’s University, Bhubaneswar, Odisha, India

    Chhabi Rani Panigrahi

  3. Cloud Computing, The University of Melbourne, Melbourne, VIC, Australia

    Rajkumar Buyya

  4. Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan

    Kuan-Ching Li

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Brahma, A., Panigrahi, S. (2020). Role of Soft Outlier Analysis in Database Intrusion Detection. In: Pati, B., Panigrahi, C., Buyya, R., Li, KC. (eds) Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 1082. Springer, Singapore. https://doi.org/10.1007/978-981-15-1081-6_41

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  • DOI: https://doi.org/10.1007/978-981-15-1081-6_41

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-1080-9

  • Online ISBN: 978-981-15-1081-6

  • eBook Packages: EngineeringEngineering (R0)

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