Abstract
Network anomalies are unusual traffic mainly induced by network attacks or network failures. Therefore it is important for network operators as end users to detect and diagnose them to protect their network. However, these anomalies keep changing in time, it is therefore important to propose detectors which can learn from the traffic and spot anomalies without relying on any previous knowledge. Unsupervised network anomaly detectors reach this goal by taking advantage of machine learning and statistical techniques to spot the anomalies. There exists many unsupervised network anomaly detectors in the literature. Each algorithm puts forward its good detection performance, therefore it is difficult to select one detector among the large set of available detectors. Therefore, this paper, presents an extensive study and assessment of a set of well known unsupervised network anomaly detectors, and underlines their strengths and weaknesses. This study overwhelms previous similar evaluation by considering for the comparison some new, original and of premier importance parameters as detection similarity, detectors sensitivity and curse of dimensionality, together with the classical detection performance, and execution time parameters.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
KDD Cup 1999 Data. http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html. Accessed 03 Feb 2016
Bahrololum, M., Khaleghi, M.: Anomaly intrusion detection system using Gaussian mixture model. In: Convergence and Hybrid Information Technology, ICCIT 2008, vol. 1, pp. 1162–1167, November 2008
Breunig, M.M., Kriegel, H.-P., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers. SIGMOD Rec. 29(2), 93–104 (2000)
Casas, P., Mazel, J., Owezarski, P.: UNADA: unsupervised network anomaly detection using sub-space outliers ranking. In: NETWORKING 2011: 10th International IFIP TC 6 Networking Conference, pp. 40–51. Springer, Heidelberg (2011)
Casas, P., Mazel, J., Owezarski, P.: Unsupervised network intrusion detection systems: detecting the unknown without knowledge. Comput. Commun. 35(7), 772–783 (2012)
Croux, C., Filzmoser, P., Oliveira, M.R.: Algorithms for projection-pursuit robust principal component analysis. Chemometr. Intell. Lab. Syst. 87(2), 218–225 (2007)
Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A Density-based Algorithm for Discovering Clusters in Large Spatial Databases with Noise, pp. 226–231. AAAI Press, Portland (1996)
Fontugne, R., Borgnat, P., Abry, P., Fukuda, K.: MAWILab: combining diverse anomaly detectors for automated anomaly labeling and performance benchmarking. In: ACM CoNEXT 2010, Philadelphia, PA (2010)
Zimek, A., Kriegel, H.-P., Kröger, P.: Outlier detection techniques. In: Tutorial Notes: SIAM SDM 2010, Columbus, Ohio (2010)
Jensen, D.R., Solomon, H.: A Gaussian approximation to the distribution of a definite quadratic form. J. Am. Stat. Assoc. 67(340), 898–902 (1972)
Julisch, K.: Clustering intrusion detection alarms to support root cause analysis. ACM Trans. Inf. Syst. Secur. 6, 443–471 (2003)
Kind, A., Stoecklin, M.P., Dimitropoulos, X.: Histogram-based traffic anomaly detection. IEEE Trans. Netw. Serv. Manage. 6(2), 110–121 (2009)
Kriegel, H.-P., Kröger, P., Schubert, E., Zimek, A.: Outlier detection in axis-parallel subspaces of high dimensional data. In: Advances in Knowledge Discovery and Data Mining: 13th Pacific-Asia Conference, pp. 831–838. Springer, Heidelberg (2009)
Kwitt, R., Hofmann, U.: Unsupervised anomaly detection in network traffic by means of robust PCA. In: Computing in the Global Information Technology, p. 37, March 2007
Lakhina, A., Crovella, M., Diot, C.: Diagnosing network-wide traffic anomalies. In: Proceedings of the 2004 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, SIGCOMM 2004, pp. 219–230. ACM (2004)
Lakhina, A., Crovella, M., Diot, C.: Mining anomalies using traffic feature distributions. ACM SIGCOMM Comput. Commun. Rev. 35(4), 217 (2005)
Leung, K., Leck, C.: Unsupervised anomaly detection in network intrusion detection using clusters. In: Proceedings of the Twenty-eighth Australasian Conference on Computer Science, ACSC 2005, vol. 38, pp. 333–342. Australian Computer Society, Inc, Darlinghurst (2005)
Morris, M.D.: Factorial sampling plans for preliminary computational experiments. Technometrics 33(2), 161–174 (1991)
Olusola, A.A., Oladele, A.S., Abosede, D.O.: Analysis of KDD 99 intrusion detection dataset for selection of relevance features. In: World Congress on Engineering and Computer Science, pp. 162–168 (2010)
Kriegel, H.P., Schneider, R., Seeger, B., Beckmann, N.: The R*-tree: an efficient and robust access method for points and rectangles. Sigmod Rec. 19, 322–331 (1990)
Portnoy, L., Eskin, E., Stolfo, S.: Intrusion detection with unlabeled data using clustering. In: In Proceedings of ACM CSS Workshop on Data Mining Applied to Security, pp. 5–8 (2001)
Ringberg, H., Soule, A., Rexford, J., Diot, C.: Sensitivity of PCA for traffic anomaly detection. SIGMETRICS Perform. Eval. Rev. 35(1), 109–120 (2007)
Shyu, M.-L., Chen, S.-C., Sarinnapakorn, K., Chang, L.: A novel anomaly detection scheme based on principal component classifier. In: IEEE Foundations and New Directions of Data Mining Workshop, pp. 171–179 (2003)
Sommer, R., Paxson, V.: Outside the closed world: on using machine learning for network intrusion detection. In: 2010 IEEE Symposium on Security and Privacy, 0(May), pp. 305–316 (2010)
Syarif, I., Prugel-Bennett, G., Wills, A.: Unsupervised clustering approach for network anomaly detection. In: Networked Digital Technologies: 4th International Conference. Springer, Heidelberg (2012)
Thang, T.M., Kim, J.: The anomaly detection by using DBSCAN clustering with multiple parameters. In: 2011 International Conference on Information Science and Applications (ICISA), pp. 1–5, April 2011
Tsakok, J.A., Bishop, W., Kennings, Af.: kd-Tree traversal techniques. In: Interactive Ray Tracing, p. 190, August 2008
Yasami, Y., Khorsandi, S., Mozaffari, S.P., Jalalian, A.: An unsupervised network anomaly detection approach by k-means clustering & ID3 algorithms. In: Computers and Communications, pp. 398–403, July 2008
Zimek, A., Schubert, E., Kriegel, H.-P.: A survey on unsupervised outlier detection in high-dimensional numerical data. Stat. Anal. Data Min. 5(5), 363–387 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Dromard, J., Owezarski, P. (2020). Study and Evaluation of Unsupervised Algorithms Used in Network Anomaly Detection. In: Arai, K., Bhatia, R., Kapoor, S. (eds) Proceedings of the Future Technologies Conference (FTC) 2019. FTC 2019. Advances in Intelligent Systems and Computing, vol 1070. Springer, Cham. https://doi.org/10.1007/978-3-030-32523-7_28
Download citation
DOI: https://doi.org/10.1007/978-3-030-32523-7_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32522-0
Online ISBN: 978-3-030-32523-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)