Anomaly detection in large-scale data stream networks

Data Mining and Knowledge Discovery

Volumn 28, Issue 1, 2014, Pages 145-189

  Pham, Duc Son ^a   Venkatesh, Svetha ^b   Lazarescu, Mihai ^a   Budhaditya, Saha ^b  

^a CURTIN UNIVERSITY   (Australia)

^b DEAKIN UNIVERSITY   (Australia)

Author keywords

Anomaly detection; Compressed sensing; Random projection; Residual subspace analysis; Sensor network data; Spectral methods; Stream data processing

Indexed keywords

BENCHMARKING; COMPRESSED SENSING; COMPUTATION THEORY; DATA ACQUISITION; DATA MINING; DATA STREAMS; DIGITAL STORAGE; LARGE DATASET; SENSOR NETWORKS;

COMPRESSIVE SENSING; CONVENTIONAL METHODS; PHYSICAL LIMITATIONS; RANDOM PROJECTIONS; RESIDUAL SUBSPACES; SPECTRAL METHODS; STREAM DATA PROCESSING; SURVEILLANCE APPLICATIONS;

ANOMALY DETECTION;

EID: 84891887122     PISSN: 13845810     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10618-012-0297-3     Document Type: Article

References (43)

1. Achlioptas D (2001) Database-friendly random projections. In: Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on principles of database systems. ACM, New York, pp 274-281. http://doi.acm.org/10.1145/375551.375608 (Pubitemid 32865990)
2. Adam A, Rivlin E, Shimshoni I, Reinitz D (2008) Robust real-time unusual event detection using multiple fixed-location monitors. IEEE Trans Pattern Anal Mach Intell 30:555-560 (Pubitemid 351280062)
3. Aggarwal C (2005) On abnormality detection in spuriously populated data streams. In: Proceedings of the IEEE international conference on data mining (ICDM), Houston
4. Barnett V, Lewis T (1984) Outliers in statistical data. Chichester, New York
5. Bingham E, Mannila H (2001) Random projection in dimensionality reduction: applications to image and text data. In: Proceedings of the KDD. ACM, New York, pp 245-250
6. Brand M, Oliver N, Pentland A (1997) Coupled hidden Markov models for complex action recognition. In: IEEE CVPR, San Juan, pp 994-999
7. Budhaditya S, Pham D, Lazarescu M, Venkatesh S (2009) Effective anomaly detection in sensor networks data streams. In: Proceedings of the IEEE international conference on data mining (ICDM), Miami, pp 722-727
8. Candes E, Tao T (2006) Near optimal signal recovery from random projections: universal encoding strategies. IEEE Trans Inf Theory 52:5406-5425 (Pubitemid 46445381)
9. Candes E, Romberg J, Tao T (2006) Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory 52(2):489-509 (Pubitemid 43174093)
10. Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM Comput Surv 41:Article 15
11. Chatzigiannakis V, Papavassiliou S, Grammatikou M, Maglaris B (2006) Hierarchical anomaly detection in distributed large-scale sensor networks. In: Proceedings of the 11th IEEE symposium on computers and communications (ISCC), Washington, DC, pp 761-767
12. Donoho D (2006) Compressed sensing. IEEE Trans Inf Theory 52:1289-1306
13. Drineas P, Frieze A, Kannan R, Vempala S, Vinay V (2004) Clustering large graphs via the singular value decomposition. Mach Learn 56(1):9-33
14. Drineas P, Kannan R, Mahoney M (2006) Fast Monte Carlo algorithms for matrices II: computing a low-rank approximation to a matrix. SIAM J Comput 36(1):158
15. Elad M (2007) Optimized projections for compressed sensing. IEEE Trans Signal Proc 55:5695-5702 (Pubitemid 350201203)
16. Fowler J (2009) Compressive-projection principal component analysis and the first eigenvector. In: Data compression conference, 2009, DCC'09, Snowbird. IEEE, Washington, DC, pp 223-232
17. Fujimaki R (2008) Anomaly detection support vector machine and its application to fault diagnosis. In: Proceedings of the IEEE international conference on data mining (ICDM), Washington, DC, pp 797-802
18. Geman S (1980) A limit theorem for the norm of random matrices. Ann Probab 8:252-261
19. Giatrakos N, Kotidis Y, Deligiannakis A, Vassalos V, Theodoridis Y (2010) Taco: tunable approximate computation of outliers in wireless sensor networks. In: Proceedings of the 2010 international conference on Management of data. ACM, New York, pp 279-290
20. Golub Loan V (1996) Matrix computations, 3rd edn. Johns Hopkins University Press, Baltimore
21. http://www.abilene.iu.edu/
22. http://www.cvg.rdg.ac.uk/pets2007/data.html/
23. Huang L, Nguyen X, Garofalakis M, Jordan M, Joseph A, Taft N (2007) In-network PCA and anomaly detection. In: Proceedings of NIPS, Vancouver, pp 617-624
24. Jackson J (1959) Quality control methods for several related variables. Technometrics 1:359-377
25. Jackson J (1980) Principal components and factor analysis. I - principal components. J Qual Technol 12:201-213
26. Jackson E, Mudholkar G (1979) Control procedures for residuals associated with principal component analysis. Technometrics 21(3):341-349 (Pubitemid 9472204)
27. Janakiram D, Reddy V, Kumar A (2006) Outlier detection in wireless sensor networks using Bayesian belief networks. In: Proceedings of the first international conference on communication system software and middleware, New Delhi
28. Jiang X, Cooper G (2010) A real-time temporal bayesian architecture for event surveillance and its application to patient-specific multiple disease outbreak detection. Data Min Knowl Discov 20(3):328-360
29. Koufakou A, Georgiopoulos M (2010) A fast outlier detection strategy for distributed high-dimensional data sets with mixed attributes. Data Min Knowl Discov 20(2):259-289
30. Lakhina A, Crovella M, Diot C (2004) Diagonising network-wide traffic anomalies. In: Proceedings of ACM SIGCOMM, Portland
31. Li W, Yue H, Valle-Cervantes S, Qin S (2000) Recursive PCA for adaptive process monitoring. J Process Control 10(5):471-486
32. Liu K, Kargupta H, Ryan J (2006) Random projection-based multiplicative data perturbation for privacy preserving distributed data mining. IEEE Trans Knowl Data Eng 18(1):92-106 (Pubitemid 43145091)
33. Lucas B, Kanade T (1981) An iterative image registration technique with an application to stereo vision. Proc IJCAI 81:674-679 (Pubitemid 14478744)
34. Medioni G, Cohen I, Brémond F, Hongeng S, Nevatia R (2001) Event detection and analysis from video streams. IEEE Trans Pattern Anal Mach Intell 23:873-889
35. Niebles J, Wang H, Fei-Fei L (2008) Unsupervised learning of human action categories using spatial-temporal words. Int J Comput Vis 79(3):299-318
36. Noto K, Brodley C, Slonim D (2011) Frac: a feature-modeling approach for semi-supervised and unsupervised anomaly detection. Data Min Knowl Discov 25:109-133
37. Phung D, Duong T, Venkatesh S, Bui H (2005) Topic transition detection using hierarchical hidden Markov and semi-Markov models. In: Proceedings of ACM-MM, New York, pp 11-20
38. Rabbat M, Haupt J, Singh A, Nowak R (2006) Decentralized compression and predistribution via randomized gossiping. In: Proceedings of IPSN, New York, pp 51-59
39. Strohmer T, Heath R (2003) Grassmannian frames with applications to coding and communication. Appl Comput Harmon Anal 14:257-275
40. Thottan M, Ji C (2003) Anomaly detection in IP networks. IEEE Trans Signal Process 51(8):2191-2204
41. Vempala S (2004) The random projection method. American Mathematical Society (AMS)
42. Yan J, Zhang B, Liu N, Yan S, Cheng Q, Fan W, Yang Q, Xi W, Chen Z (2006) Effective and efficient dimensionality reduction for large-scale and streaming data preprocessing. IEEE Trans Knowl Data Eng 18:320-333
43. Zhu C, Kitagawa H, Faloutsos C (2005) Example-based robust outlier detection in high dimensional datasets. In: Proceedings of ICDM, Houston