Advertisement

Hybrid Data Mining to Reduce False Positive and False Negative Prediction in Intrusion Detection System

  • Bala PalanisamyEmail author
  • Biswajit Panja
  • Priyanka Meharia
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 887)

Abstract

This paper proposes an approach of data mining machine learning methods for reducing the false positive and false negative predictions in existing Intrusion Detection Systems (IDS). It describes our proposal for building a confidential strong intelligent intrusion detection system which can save data and networks from potential attacks, having recognized movement or infringement regularly reported ahead or gathered midway. We have addressed different data mining methodologies and presented some recommended approaches which can be built together to enhance security of the system. The approach will reduce the overhead of administrators, who can be less concerned about the alerts as they have been already classified and filtered with less false positive and false negative alerts. Here we have made use of KDD-99 IDS dataset for details analysis of the procedures and algorithms which can be implemented.

Keywords

Intrusion Detection Systems Data mining Intrusion detection Anomaly detection SVM KNN ANN 

References

  1. 1.
    Xu, L., Jiang, C., Wang, J., Yuan, J., Ren, Y.: Information security in big data: privacy and data mining. IEEE Access 2, 1149–1176 (2014)CrossRefGoogle Scholar
  2. 2.
    Yu, C.H., Ward, M.W., Morabito, M., Ding, W.: Crime forecasting using data mining techniques. In: 2011 IEEE 11th International Conference on Data Mining Workshops, pp. 779–786. IEEE (2011)Google Scholar
  3. 3.
    Hajian, S., Domingo-Ferrer, J., Martinez-Balleste, A.: Discrimination prevention in data mining for intrusion and crime detection. In: 2011 IEEE Symposium on Computational Intelligence in Cyber Security (CICS), pp. 47–54. IEEE (2011)Google Scholar
  4. 4.
    Xu, J., Yu, Y., Chen, Z., Cao, B., Dong, W., Guo, Y., Cao, J.: Mobsafe: cloud computing based forensic analysis for massive mobile applications using data mining. Tsinghua Sci. Technol. 18(4), 418–427 (2013)CrossRefGoogle Scholar
  5. 5.
    Bhattacharyya, S., Jha, S., Tharakunnel, K., Westland, J.C.: Data mining for credit card fraud: a comparative study. Decis. Support Syst. 50(3), 602–613 (2011)CrossRefGoogle Scholar
  6. 6.
    Hu, Y., Panda, B.: A data mining approach for database intrusion detection. In: Proceedings of the 2004 ACM Symposium on Applied Computing, pp. 711–716. ACM (2004)Google Scholar
  7. 7.
    Ravisankar, P., Ravi, V., Rao, G.R., Bose, I.: Detection of financial statement fraud and feature selection using data mining techniques. Decis. Support Syst. 50(2), 491–500 (2011)CrossRefGoogle Scholar
  8. 8.
    Erskine, J.R., Peterson, G.L., Mullins, B.E., Grimaila, M.R.: Developing cyberspace data understanding: using CRISP-DM for host-based IDS feature mining. In: Proceedings of the Sixth Annual Workshop on Cyber Security and Information Intelligence Research, p. 74. ACM (2010)Google Scholar
  9. 9.
    Lee, W., Stolfo, S.J., Mok, K.W.: A data mining framework for building intrusion detection models. In: Proceedings of the 1999 IEEE Symposium on Security and Privacy, 1999, pp. 120–132. IEEE (1999)Google Scholar
  10. 10.
    Feng, W., Zhang, Q., Hu, G., Huang, J.X.: Mining network data for intrusion detection through combining SVMs with ant colony networks. Future Gener. Comput. Syst. 37, 127–140 (2014)CrossRefGoogle Scholar
  11. 11.
    Portnoy, L., Eskin, E., Stolfo, S.: Intrusion detection with unlabeled data using clustering. In: Proceedings of ACM CSS Workshop on Data Mining Applied to Security (DMSA-2001) (2001)Google Scholar
  12. 12.
    Julisch, K., Dacier, M.: Mining intrusion detection alarms for actionable knowledge. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 366–375. ACM (2002)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Bala Palanisamy
    • 1
    Email author
  • Biswajit Panja
    • 1
  • Priyanka Meharia
    • 1
  1. 1.Department of Computer ScienceEastern Michigan UniversityYpsilantiUSA

Personalised recommendations