Statistical wavelet-based anomaly detection in big data with compressive sensing

Eurasip Journal on Wireless Communications and Networking

Volumn 2013, Issue 1, 2013, Pages

  Wang, Wei ^a   Lu, Dunqiang ^a   Zhou, Xin ^a   Zhang, Baoju ^a   Mu, Jiasong ^a  

^a TIANJIN NORMAL UNIVERSITY   (China)

Author keywords

Anomaly detection; Big data; Compressive sensing; Through wall human detection

Indexed keywords

EID: 84894166767     PISSN: 16871472     EISSN: 16871499     Source Type: Journal    
DOI: 10.1186/1687-1499-2013-269     Document Type: Article

References (29)

1. V Chandola, A Banerjee, V Kumar, Anomaly detection: a survey. ACM Comput. Surv. 41(3), 1-72 (2009)
2. CT Chou, R Rana, W Hu, Energy efficient information collection in wireless sensor networks using adaptive compressive sensing, in IEEE 34th Conference on Local Computer Networks, Zurich, Switzerland, 20-23 October 2009, pp. 443-450
3. J Wang, S Tang, B Yin et al., Data gathering in wireless sensor networks through intelligent compressive sensing, in Proceedings IEEE INFOCOM, Orlando, FL, 25-30 March 2012, pp. 603-611
4. DL Donoho, Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289-1306 (2006)
5. E Candès, Compressive sampling, in Proceedings of International Congress of Mathematicians (European Mathematical Society Publishing House, Madrid, 2006), pp. 1433-1452
6. CM Yu, CS Lu, SY Kuo, CSI: compressed sensing-based clone identification in sensor networks, in 8th IEEE International Workshop on Sensor Networks and Systems for Pervasive Computing 2012, Lugano, 9-23 March 2012, pp. 296-301
7. Y Xia, Z Zhao, H Zhang, Distributed anomaly event detection in wireless networks using compressed sensing, in Proc IEEE ISCIT 2011, Hangzhou, 12-14 October 2011, pp. 250-255
8. S Budhaditya, DS Pham, M Lazarescu, S Venkatesh, Effective anomaly detection in sensor networks data streams, in 9th IEEE International Conference on Data Mining, Miami, FL, 6-9 December 2009, pp. 722-727
9. DS Pham, S Venkatesh, M Lazarescu, S Budhadity, Anomaly detection in large-scale data stream networks. Data Min. Knowl. Discov. (2012). doi: 10.1007/s10618-012-0297-3
10. Q Ding, ED Kolaczyk, A compressed PCA subspace method for anomaly detection in high-dimensional data. IEEE Trans. Inf. Theory 59(11), 7419-7422 (2012)
11. DS Pham, B Saha, M Lazarescu, S Venkatesh, Scalable network-wide anomaly detection using compressed data, Technical report (Institute for Multisensor Processing and Content Analysis, Department of Computing, Curtin University of Technology, 2009)
12. Z Sun, CC Chang, Statistical wavelet-based method for structural health monitoring. J. Struct. Eng. 130(7), 1055-1062 (2004) (Pubitemid 40265006)
13. E Candès, M Wakin, An introduction to compressive sampling. IEEE Signal Process Mag. 25, 21-30 (2008)
14. L Xu, Q Liang, X Cheng, D Chen, Compressive sensing in distributed radar sensor networks using pulse compression waveforms. EURASIP J. Wirel. Commun. Netw. (2013). doi: 10.1186/1687-1499-2013-36
15. L Xu, Q Liang, Zero correlation zone sequence pair sets for MIMO radar. IEEE Trans. Aerosp. Electron. Syst. 48(3), 2100-2113 (2012)
16. L Xu, Q Liang, Orthogonal pulse compression codes for MIMO radar system, in IEEE Globecom, Miami, FL, 6-10 Dec. 2010
17. L Xu, Q Liang, Waveform design and optimization in radar sensor network, in IEEE Globecom, Miami, FL, 6-10 Dec. 2010
18. Q Liang, JM Mendel, Design interval type-2 fuzzy logic systems using SVDQR method: rule reduction. Int. J. Intell. Syst. 15(10), 939-957 (2000)
19. S Singh, Q Liang, D Chen, L Sheng, Sense through wall human detection using UWB radar. EURASIP J. Wirel. Commun. Netw. 2011, 20 (2011)
20. D Kocur, J Rovňáková, M Švecová, Through wall tracking of moving targets by M-sequence UWB radar. Stud. Comput. Intell. 243, 349-364 (2009)
21. W Wang, X Zhou, B Zhang, J Mu, Anomaly detection in big data from UWB radars. Secur. Comm. Network (2013). doi: 10.1002/sec.745
22. L Xu, Q Liang, Radar sensor network using a set of new ternary codes: theory and application. IEEE Sensors J. 11(2), 439-450 (2011)
23. Q Liang, Situation understanding based on heterogeneous sensor networks and human-inspired favor weak fuzzy logic system. IEEE Syst. J. 5(2), 156-163 (2011)
24. Q Liang, Biologically-inspired target recognition in radar sensor networks. EURASIP J. Wirel. Commun. Netw. 2010, 523435 (2010)
25. Q Liang, X Cheng, S Samn, NEW: network-enabled electronic warfare for target recognition. IEEE Trans. Aerosp. Electron. Syst. 46(22), 558-568 (2010)
26. Q Liang, X Cheng, KUPS: knowledge-based ubiquitous and persistent sensor networks for threat assessment. IEEE Trans. Aerosp. Electron. Syst 44(3), 1-7 (2008). 1060-1069
27. Q Liang, Automatic target recognition using waveform diversity in radar sensor networks. Pattern Recogn. Lett. 29(2), 377-381 (2008) (Pubitemid 350250920)
28. Q Liang, Radar sensor networks: algorithms for waveform design and diversity with application to ATP with delay-doppler uncertainty. EURASIP J. Wirel. Commun. Netw. 1-9 (2007)
29. Q Liang, Waveform design and diversity in radar sensor networks: theoretical analysis and application to automatic target recognition, in IEEE Third Annual Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON 2006), Reston, VA, 28-28 September 2006