Skip to main content

Advertisement

Springer Nature Link
Log in

Detecting anomalies from high-dimensional wireless network data streams: a case study

  • Focus
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

In this paper, we study the problem of anomaly detection in wireless network streams. We have developed a new technique, called Stream Projected Outlier deTector (SPOT), to deal with the problem of anomaly detection from multi-dimensional or high-dimensional data streams. We conduct a detailed case study of SPOT in this paper by deploying it for anomaly detection from a real-life wireless network data stream. Since this wireless network data stream is unlabeled, a validating method is thus proposed to generate the ground-truth results in this case study for performance evaluation. Extensive experiments are conducted and the results demonstrate that SPOT is effective in detecting anomalies from wireless network data streams and outperforms existing anomaly detection methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Aggarwal CC (2005) On abnormality detection in spuriously populated data streams. In: 2005 SIAM international conference on data mining (SDM’05), Newport Beach, pp 84–93

  • Aggarwal CC, Yu PS (2001) Outlier detection in high dimensional data. In: 2001 ACM SIGMOD international conference on management of data (SIGMOD’01). Santa Barbara, pp 37–46

  • Aggarwal CC, Yu PS (2005) An effective and efficient algorithm for high-dimensional outlier detection. VLDB J 14:211–221

    Article  Google Scholar 

  • Aggarwal CC, Han J, Wang J, Yu PS (2003) A framework for clustering evolving data streams. In: 2003 very large database conference (VLDB’03), Berlin, Germany, pp 81–92

  • Aggarwal CC, Han J, Wang J, Yu PS (2004) A framework for projected clustering of high dimensional data streams. In: 2004 Very large database conference (VLDB’04), Toronto, Canada, pp 852–863

  • Angiulli F, Pizzuti C (2002) Fast outlier detection in high dimensional spaces. In: 2002 European conference on principles of data mining and knowledge discovery (PKDD’02). Helsinki, Finland, pp 15–26

  • Barbara D (2002) Requirements for clustering data streams. ACM SIGKDD Explorations Newsletter, vol 3, Issue 2. ACM Press, London, pp 23–27

  • Balazinska M, Castro P (2003) Characterizing mobility and network usage in a corporate wireless local-area network. In: 2003 International conference on mobile systems, applications, and services (MobiSys’03), San Francisco, CA, USA, pp 232–239

  • Breuning M, Kriegel HP, Ng R, Sander J (2000) LOF: identifying density-based local outliers. In: 2000 ACM SIGMOD international conference on management of data (SIGMOD’00), Dallas, Texas, USA, pp 93–104

  • Boudjeloud L, Poulet F (2005) Visual interactive evolutionary algorithm for high dimensional data clustering and outlier detection. In: 9th Pacific-Asia conference on advances in knowledge discovery and data mining (PAKDD’05). Hanoi, Vietnam, pp 426–431

  • Eskin E, Arnold A, Prerau M, Portnoy L, Stolfo S (2002) A geometric framework for unsupervised anomaly detection: detecting intrusions in unlabeled data. In: Applications of Data Mining in Computer Security, pp 34–42

  • Guttman A (1984) R-trees: a dynamic index structure for spatial searching. In: 1984 ACM SIGMOD international conference on management of data (SIGMOD’84). Boston, Massachusetts, pp 47–57

  • Guha S, Rastogi R, Shim K (1998) CURE: an efficient clustering algorithm for large databases. In: 1998 ACM SIGMOD international conference on management of data (SIGMOD’98). Seattle, WA, USA, pp 73–84

  • Han J, Kamber M (2000) Data mining: concepts and techniques. Morgan Kaufman Publishers,

  • Jin W, Tung AKH, Han J, Wang W (2006) Ranking outliers using symmetric neighborhood relationship. 2006 Pacific-Asia conference on knowledge discovery and data mining (PAKDD’06), Singapore, pp 577–593

  • Knorr EM, Ng R (1998) Algorithms for mining distance-based outliers in large dataset. In: 1998 Very large database conference (VLDB’98). New York, NY, USA, pp 392–403

  • Knorr EM, Ng R (1999) Finding intentional knowledge of distance-based outliers. In: 1999 Very large database conference (VLDB’99), Edinburgh, Scotland, pp 211–222

  • Khoshgoftaar TM, Nath SV, Zhong S (2005) Intrusion detection in wireless networks using clusterings techniques with expert analysis. In: The fourth international conference on machine leaning and applications (ICMLA’05), Los Angeles, CA, USA, pp 54–63

  • Kaufman L, Rousseeuw PJ (1990) Finding groups in data: an introduction to cluster analysis. Wiley, New York

    Google Scholar 

  • Nguyen HV, Gopalkrishnan V (2009) Efficient pruning schemes for distance-based outlier detection. In: 2009 European conference on machine learning and knowledge discovery in databases (ECML/PKDD’09), Bled, Slovinia, pp 160–175

  • Pokrajac D, Lazarevic A, Latecki L (2007) Incremental local outlier detection for data streams. In: IEEE symposiums on computational intelligence and data mining (CIDM’07). Honolulu, Hawaii, USA, pp 504–515

  • Palpanas T, Papadopoulos D, Kalogeraki V, Gunopulos D (2003) Distributed deviation detection in sensor networks. SIGMOD Record 32(4):77–82

    Article  Google Scholar 

  • Ramaswamy S, Rastogi R, Kyuseok S (2000) efficient algorithms for mining outliers from large data sets. In: 2000 ACM SIGMOD international conference on management of data (SIGMOD’00). Dallas Texas, USA, pp 427–438

  • Tang J, Chen Z, Fu A, Cheung DW (2002) Enhancing effectiveness of outlier detections for low density patterns. In: 2002 Pacific-Asia conference on advances in knowledge discovery and data mining (PAKDD’02), Taipei, Taiwan, pp 535–548

  • Wang W, Zhang J, Wang H (2005) Grid-ODF: detecting outliers effectively and efficiently in large multi-dimensional databases. In: 2005 International conference on computational intelligence and security (CIS’05), Xi’an, China, pp 765–770

  • Wang B, Xiao G, Yu H, Yang X (2009) Distance-based outlier detection on uncertain data. In: 2009 Ninth IEEE international conference on computer and information technology, Xiamen, China, pp 293–298

  • Zhu C, Kitagawa H, Faloutsos C (2005) Example-based robust outlier detection in high dimensional datasets. In: 2005 IEEE international conference on data mining (ICDM’05), Houston, Texas, USA, pp 829–832

  • Zhu C, Kitagawa H, Papadimitriou S, Faloutsos C (2004) OBE: outlier by example. In: 2004 Pacific-Asia conference on advances in knowledge discovery and data mining (PAKDD’04), Sydney, Australia, pp 222–234

  • Zhang J, Lou M, Ling TW, Wang H (2004) HOS-Miner: a system for detecting outlying subspaces of high-dimensional data. In: 2004 Very large database conference (VLDB’04), Toronto, Canada, pp 1265–1268

  • Zhang J, Wang H (2006) Detecting outlying subspaces for high-dimensional data: the new task, algorithms and performance. In: Knowledge and information systems (KAIS), pp 333–355

  • Zhang J, Gao Q, Wang H (2006) A novel method for detecting outlying subspaces in high-dimensional databases using genetic algorithm. In: 2006 International conference on data mining (ICDM’06), Hong Kong, China, pp 731–740

  • Zhong C, Lin X, Zhang M (2009) A local outlier detection approach based on graph-cut. In: 2009 International joint conference on computational sciences and optimization, Sanya, China, pp 714–718

Download references

Author information

Authors and Affiliations

  1. University of Southern Queensland, Toowoomba, QLD, Australia

    Ji Zhang & Hua Wang

  2. Dalhousie University, Halifax, NS, Canada

    Qigang Gao

  3. Saint Mary’s University, Halifax, NS, Canada

    Hai Wang

Authors
  1. Ji Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qigang Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hai Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hua Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ji Zhang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhang, J., Gao, Q., Wang, H. et al. Detecting anomalies from high-dimensional wireless network data streams: a case study. Soft Comput 15, 1195–1215 (2011). https://doi.org/10.1007/s00500-010-0575-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-010-0575-1

Keywords