Dataset Analysis for Anomaly Detection on Critical Infrastructures

German Lopez-Civera, Enrique de la Hoz

Abstract

Anomaly Detection techniques allow to create robust security measures that provides early detection and are able to identify novel attacks that could not be prevented otherwise. Datasets represent a critical component in the process of designing and evaluating any kind of anomaly detection method. For this reason, in this paper we present the evaluation of two datasets showing the dependencies that arise between the techniques employed and the dataset itself. We also describe the characteristics that have to be taken into account while selecting a dataset to evaluate a detection algorithm in a critical infrastructure context.

References

  1. Bhuyan, M. H., Bhattacharyya, D. K., and Kalita, J. K. (2015). Towards generating real-life datasets for network intrusion detection. I. J. Network Security, 17(6):683-701.
  2. Brown, C., Cowperthwaite, A., Hijazi, A., and Somayaji, A. (2009). Analysis of the 1999 darpa/lincoln laboratory ids evaluation data with netadhict. In 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, pages 1-7.
  3. Coull, S. E., Monrose, F., Reiter, M. K., and Bailey, M. (2009). The challenges of effectively anonymizing network data. In Conference For Homeland Security, 2009. CATCH'09. Cybersecurity Applications & Technology, pages 230-236. IEEE.
  4. Hunter, J. D. (2007). Matplotlib: A 2d graphics environment. Computing In Science & Engineering, 9(3):90- 95.
  5. McHugh, J. (2000). The 1998 lincoln laboratory ids evaluation. In Recent Advances in Intrusion Detection, pages 145-161. Springer.
  6. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825-2830.
  7. Rinaldi, S. M., Peerenboom, J. P., and Kelly, T. K. (2001). Identifying, understanding, and analyzing critical infrastructure interdependencies. IEEE Control Systems, 21(6):11-25.
  8. Shiravi, A., Shiravi, H., Tavallaee, M., and Ghorbani, A. A. (2012). Toward developing a systematic approach to generate benchmark datasets for intrusion detection. Comput. Secur., 31(3):357-374.
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Paper Citation


in Harvard Style

Lopez-Civera G. and de la Hoz E. (2016). Dataset Analysis for Anomaly Detection on Critical Infrastructures . In - DCCI, (ICETE 2016) ISBN , pages 0-0. DOI: 10.5220/0006017701510158


in Bibtex Style

@conference{dcci16,
author={German Lopez-Civera and Enrique de la Hoz},
title={Dataset Analysis for Anomaly Detection on Critical Infrastructures},
booktitle={ - DCCI, (ICETE 2016)},
year={2016},
pages={},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006017701510158},
isbn={},
}


in EndNote Style

TY - CONF
JO - - DCCI, (ICETE 2016)
TI - Dataset Analysis for Anomaly Detection on Critical Infrastructures
SN -
AU - Lopez-Civera G.
AU - de la Hoz E.
PY - 2016
SP - 0
EP - 0
DO - 10.5220/0006017701510158