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Artificial Intelligence Review

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01.04.2015

One-class support vector machines: analysis of outlier detection for wireless sensor networks in harsh environments

verfasst von: Nauman Shahid, Ijaz Haider Naqvi, Saad Bin Qaisar

Erschienen in: Artificial Intelligence Review | Ausgabe 4/2015

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Abstract

Machine learning, like its various applications, has received a great interest in outlier detection in Wireless Sensor Networks. Support Vector Machines (SVM) are a special type of Machine learning techniques which are computationally inexpensive and provide a sparse solution. This work presents a detailed analysis of various formulations of one-class SVMs, like, hyper-plane, hyper-sphere, quarter-sphere and hyper-ellipsoidal. These formulations are used to separate the normal data from anomalous data. Various techniques based on these formulations have been analyzed in terms of a number of characteristics for harsh environments. These characteristics include input data type, spatio-temporal and attribute correlations, user specified thresholds, outlier types, outlier identification(event/error), outlier degree, susceptibility to dynamic topology, non-stationarity and inhomogeneity. A tabular description of improvement and feasibility of various techniques for deployment in the harsh environments has also been presented.

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Abe S (2010) Support vector machines for pattern classification. Springer New York Inc

Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E (2002) Wireless sensor networks: a survey. Comput Netw 38:393–422CrossRef

Akyildiz IF, Akan Özgür B (2003) Interplanetary internet: state-of-the-art and research challenges. Comput Netw 43:75–112CrossRefMATH

Aly M (2005) Survey on multiclass classification methods. Neural Netw pp 1–9

Ambwani T (2003) Multi class support vector machine implementation to intrusion detection. In: Neural networks, 2003. Proceedings of the international joint conference on, vol. 3. IEEE, pp 2300–2305

Bahrepour M, Meratnia N, Havinga PJM (2010) Fast and accurate residential fire detection using wireless sensor networks. Environ Eng Manag J 9(2):215–221

Bahrepour M, Meratnia N, Poel M, Taghikhaki Z, Havinga PJM (November 2010) Distributed event detection in wireless sensor networks for disaster management. In: International conference on intelligent networking and collaborative systems, INCoS (2010) Thessaloniki. Greece. IEEE Computer Society, USA, pp 507–512

Bahrepour M, van der Zwaag BJ, Meratnia N, Havinga PJM (2010) Fire data analysis and feature reduction using computational intelligence methods. In: Phillips-Wren G, Jain LC, Nakamatsu K, (eds) Advances in intelligent decision technologies. In: Proceedings of the second KES international symposium IDT 2010, Baltimore, Maryland, USA ser. Smart innovation, systems and technologies vol 4. Springer, Berlin Heidelberg, pp 289–298

Bahrepour M, Zhang Y, Meratnia N, Havinga PJM (December 2009) Use of event detection approaches for outlier detection in wireless sensor networks. In: Proceedings of symposium on theoretical and practical aspects of large-scale wireless sensor networks, The 5th international conference on intelligent sensors, sensor networks and information processing (2009) (ISSNIP 2009), Melbourne. Australia. IEEE Press, Victorian, pp 439–444

Barnett V, Lewis T (1994) Outliers in statistical data. Wiley, New YorkMATH

Bettencourt LMA, Hagberg AA, Larkey LB (Jun 2007) Separating the wheat from the chaff: practical anomaly detection schemes in ecological applications of distributed sensor networks. In: Distributed computing in sensor systems (DCOSS 2007). Santa Fe, NM USA, pp 223–239

Bezdek J, Rajasegarar S, Moshtaghi M, Leckie C, Palaniswami M, Havens T (2011) Anomaly detection in environmental monitoring networks [application notes]. IEEE Comput Intell Mag 6(2):52–58CrossRef

Bezdek J, Havens T, Keller J, Leckie C, Park L, Palaniswami M, Rajasegarar S (July 2010) Clustering elliptical anomalies in sensor networks. In: Fuzzy systems (FUZZ), 2010 IEEE international conference on, pp 1–8

Bhuse V, Gupta A (2006) Anomaly intrusion detection in wireless sensor networks. J High Speed Netw 15:33–51

Bishop C (2006) Pattern recognition and machine learning, ser. Information science and statistics. Springer, [Online]. Available: http://books.google.com.pk/books?id=kTNoQgAACAAJ

Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge university press, CambridgeCrossRefMATH

Branch J, Szymanski B, Giannella C, Wolff R, Kargupta H (2006) In-network outlier detection in wireless sensor networks. In: Distributed computing systems, 2006. ICDCS 2006. 26th IEEE international conference on, p 51

Bredensteiner E, Bennett K (1999) Multicategory classification by support vector machines. Comput Optim Appl 12(1):53–79MATHMathSciNet

Cardell-Olivera R, Kranza M, Smettemb K, Mayerc K (2005) A reactive soil moisture sensor network: design and field evaluation. Int J Distrib Sens Netw 1(2):149–162CrossRef

Chandola V, Banerjee A, Kumar V (July 2009) Anomaly detection: a survey, ACM Comput. Surv., vol. 41, pp 15:1–15:58. [Online]. Available: http://doi.acm.org/10.1145/1541880.1541882

Chen J, Kher S, Somani A (2006) Distributed fault detection of wireless sensor networks. In: Proceedings of the 2006 workshop on dependability issues in wireless ad hoc networks and sensor networks, ser. DIWANS ’06. New York, NY, USA: ACM, pp 65–72. [Online]. Available: http://doi.acm.org/10.1145/1160972.1160985

Cheong S, Oh S, Lee S (2004) Support vector machines with binary tree architecture for multi-class classification. Neural Inf Process Lett Rev 2(3):47–51

da Silva APR, Martins MHT, Rocha BPS, Loureiro AAF, Ruiz LB, Wong HC (2005) Decentralized intrusion detection in wireless sensor networks. In: Proceedings of the 1st ACM international workshop on quality of service & security in wireless and mobile networks, ser. Q2SWinet ’05. New York, NY, USA: ACM, pp 16–23. [Online]. Available: http://doi.acm.org/10.1145/1089761.1089765

Dario IA, Akyildiz IF, Pompili D, Melodia T (2005) Underwater acoustic sensor networks: research challenges. Ad Hoc Netw Elsevier 3:257–279CrossRef

Dereszynski E, Dietterich T (2011) Spatiotemporal models for data-anomaly detection in dynamic environmental monitoring campaigns. ACM Trans Sens Netw (TOSN) 8(1):3

Ding M, Cheng X (april 2009) Robust event boundary detection in sensor networks–a mixture model based approach. In: INFOCOM 2009, IEEE, pp 2991–2995

Ding M, Chen D, Xing K, Cheng X (march 2005) Localized fault-tolerant event boundary detection in sensor networks. In: INFOCOM 2005. 24th annual joint conference of the IEEE computer and communications societies. Proceedings IEEE, vol. 2, pp 902–913 vol. 2

Ding X, Li Y, Belatreche A, Maguire L (2012) Constructing minimum volume surfaces using level set methods for novelty detection. In: Neural networks (IJCNN), The 2012 international joint conference on. IEEE, pp 1–6

Elisseeff A, Weston J (2002) Kernel methods for multi-labelled classification and categorical regression problems. Adv Neural Inf Process Syst 14:681–687

Elnahrawy E, Nath B (2004) Context-aware sensors. European workshop on wireless sensor, networks, In, pp 77–93

Franc V, Hlavác V (2002) Multi-class support vector machine. In: Pattern Recognition, 2002. Proceedings. 16th International Conference on, vol. 2. IEEE, pp 236–239

Ganguly AR (2008) Knowledge discovery from sensor data. CRC Press, Boca RatonCrossRef

García-Hernández CF, Ibargüengoytia-González PH, García-Hernández J, Pérez-Díaz JA (2007) Wireless sensor networks and applications: a survey. IJCSNS Int J Comput Sci Netw Secur 7(3):264–273

George S, Zhou W, Chenji H, Won M, Lee Y, Pazarloglou A, Stoleru R, Barooah P (2010) Distressnet: a wireless ad hoc and sensor network architecture for situation management in disaster response. IEEE Commun Mag 48(3):128–136CrossRef

Giatrakos N, Kotidis Y, Deligiannakis A (2010) Pao: power-efficient attribution of outliers in wireless sensor networks. In: Proceedings of the seventh international workshop on data management for sensor networks. ACM, pp 33–38

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, pp 279–290

Gomez-Verdejo V, Arenas-Garcia J, Lazaro-Gredilla M, Navia-Vazquez A (2011) Adaptive one-class support vector machine. IEEE Trans Signal Process 59(6):2975–2981CrossRefMathSciNet

Han J, Kamber M (2006) Data mining: concepts and techniques. Morgan Kaufmann

Hao P, Chiang J, Lin Y (2009) A new maximal-margin spherical-structured multi-class support vector machine. Appl Intell 30(2):98–111CrossRef

Hassan A et al. (2011) A heuristic approach for sensor network outlier detection. Int J Res Rev Wirel Sens Netw (IJRRWSN) 1(4):

Herbrich R (2002) Learning kernel classifiers: theory and algorithms. The MIT press, Cambridge

Hill DJ, Minsker BS, Amir E (2007) Real-time bayesian anomaly detection for environmental sensor data. In: In Proceedings of the 32nd conference of IAHR

Hofmann T, Schölkopf B, Smola A (2008) Kernel methods in machine learning. Ann Stat, pp 1171–1220

Hsu C, Lin C (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 13(2):415–425CrossRef

http://connekt.seecs.nust.edu.pk/SAHSE.php

http://www.genuki.org.uk/big/eng/LAN/Haydock/WoodPitExplosion.html

http://www.humanite.fr/2006-03-10Societe-Catastrophe-de-Courrieres-une-expression-impropre

http://www.msha.gov/MSHAINFO/FactSheets/MSHAFCT8.HTM

Janakiram D, Adi Mallikarjuna Reddy V, Phani Kumar A (2006) Outlier detection in wireless sensor networks using bayesian belief networks. In: Communication system software and middleware, 2006. Comsware 1–6

John GH (1995) Robust decision trees: removing outliers from databases. In: In Knowledge discovery and data mining. AAAI Press, pp 174–179

Jun MC, Jeong H, Kuo C-CJ (2005) Distributed spatio-temporal outlier detection in sensor networks

Katdare M (2011) Pattern based outlier detection in mixed-attribute datasets

Keally M, Zhou G, Xing G (2010) Watchdog: confident event detection in heterogeneous sensor networks. In: Real-time and embedded technology and applications symposium (RTAS), (2010) 16th IEEE. IEEE 2010:279–288

Kecman V (2005) Support vector machines-an introduction, support vector machines: theory and applications, pp 605–605

Keerthi S, Sundararajan S, Chang K, Hsieh C, Lin C (2008) A sequential dual method for large scale multi-class linear svms. In: Proceeding of the 14th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 408–416

Krishnamachari B, Iyengar S (2004) Distributed bayesian algorithms for fault-tolerant event region detection in wireless sensor networks. IEEE Trans Comput 53(3):241–250CrossRef

Laskov P, Schäfer C, Kotenko I (2004) Intrusion detection in unlabeled data with quarter-sphere support vector machines. In: Proceedings DIMVA, pp 71–82

Lazarevic A, Ozgur A, Ertoz L, Srivastava J, Kumar V (2003) A comparative study of anomaly detection schemes in network intrusion detection. In: In proceedings of the third SIAM international conference on data mining

Liu Z (2011) A method of svm with normalization in intrusion detection. Procedia Environ Sci 11:256–262CrossRef

Liu S, Liu Y, Wang B (2007) An improved hyper-sphere support vector machine. In: Natural computation, 2007. ICNC 2007. Third international conference on, vol. 1. IEEE, pp 497–500

Liu C, Yang Y, Tang C (2010) An improved method for multi-class support vector machines. In: Measuring technology and mechatronics automation (ICMTMA), 2010 international conference on, vol. 1. IEEE, pp 504–508

Luo X, Dong M, Huang Y (2006) On distributed fault-tolerant detection in wireless sensor networks. IEEE Trans Comput 55(1):58–70CrossRef

Ma S, Wang J, Liu Z, Jiang H (2013) Density-based distributed elliptical anomaly detection in wireless sensor networks. Appl Mech Mater 249:226–230CrossRef

Madden S, Franklin MJ, Hellerstein JM, Hong W (2002) Tag: a tiny aggregation service for ad-hoc sensor networks, in IN OSDI

Mainwaring A, Culler D, Polastre J, Szewczyk R, Anderson J (2002) Wireless sensor networks for habitat monitoring. In: Proceedings of the 1st ACM international workshop on wireless sensor networks and applications, ser. WSNA ’02. New York, NY, USA: ACM, pp 88–97. [Online]. Available: http://doi.acm.org/10.1145/570738.570751

Mayoraz E, Alpaydin E (1999) Support vector machines for multi-class classification. Engineering Applications of Bio-Inspired Artificial, Neural Networks, pp 833–842

McDonald D, Madria S, Ercal F, Birmingham R, Lake T (2012) Ctod: collaborative tree-based outlier detection in wireless sensor networks. In: Proceedings of the 10th ACM international symposium on Mobility management and wireless access. ACM, pp 1–10

Misra P, Kanhere S, Ostry D, Jha S (2010) Safety assurance and rescue communication systems in high-stress environments: a mining case study. IEEE Commun Mag 48(4):66–73CrossRef

Moshtaghi M, Havens T, Bezdek J, Park L, Leckie C, Rajasegarar S, Keller J, Palaniswami M (2011) Clustering ellipses for anomaly detection. Pattern Recognit 44(1):55–69CrossRefMATH

Moshtaghi M, Bezdek J, Havens T, Leckie C, Karunasekera S, Rajasegarar S, Palaniswami M (2012) Streaming analysis in wireless sensor networks. Wirel Commun Mob Comput

Moshtaghi M, Leckie C, Karunasekera S, Bezdek J, Rajasegarar S, Palaniswami M (2011) Incremental elliptical boundary estimation for anomaly detection in wireless sensor networks in Data Mining (ICDM), 2011 IEEE 11th International Conference on. IEEE, pp 467–476

Moshtaghi M, Rajasegarar S, Leckie C, Karunasekera S (2011) An efficient hyperellipsoidal clustering algorithm for resource-constrained environments. Pattern Recognit

Navia-Vazquez A, Gutierrez-Gonzalez D, Parrado-Hernandez E, Navarro-Abellan J (2006) Distributed support vector machines. IEEE Trans Neural Netw 17(4):1091–1097CrossRef

Nguyen X, Huang L, Joseph A (2008) Support vector machines, data reduction, and approximate kernel matrices. Mach Learn Knowl Discov Databases, pp 137–153

Ni L, Liu Y, Lau YC, Patil A, (march 2003) Landmarc: indoor location sensing using active rfid, in pervasive computing and communications, (2003) PerCom 2003. In: Proceedings of the first IEEE international conference on, pp 407–415

O’Reilly C, Gluhak A, Imran M, Rajasegarar S (2012) Online anomaly rate parameter tracking for anomaly detection in wireless sensor networks. In: Sensor, Mesh and Ad Hoc communications and networks (SECON), 2012 9th annual IEEE communications society conference on. IEEE, pp 191–199

Ozdemir S, Xiao Y (2011) Outlier detection based fault tolerant data aggregation for wireless sensor networks. In: Application of information and communication technologies (AICT), 2011 5th international conference on. IEEE, pp 1–5

Ozdemir S, Xiao Y (2012) Ftda: outlier detection-based fault-tolerant data aggregation for wireless sensor networks. Secur Commun Netw

Palpanas T, Papadopoulos D, Kalogeraki V, Gunopulos D (December 2003) Distributed deviation detection in sensor networks, SIGMOD Rec., vol. 32, pp 77–82. [Online]. Available: http://doi.acm.org/10.1145/959060.959074

Paschalidis IC, Chen Y (September 2010) Statistical anomaly detection with sensor networks. ACM Trans. Sen. Netw. vol. 7, pp. 17:1–17:23. [Online]. Available: http://doi.acm.org/10.1145/1824766.1824773

Pham N, Pagh R (2012) A near-linear time approximation algorithm for angle-based outlier detection in high-dimensional data

Phua C, Lee V, Smith K, Gayler R (2010) A comprehensive survey of data mining-based fraud detection research. Arxiv, preprint arXiv:1009.6119

Rajasegarar S, Leckie C, Palaniswami M (2008) Anomaly detection in wireless sensor networks. IEEE Wirel Commun 15(4):34–40CrossRef

Rajasegarar S, Leckie C, Bezdek J, Palaniswami M (2010) Centered hyperspherical and hyperellipsoidal one-class support vector machines for anomaly detection in sensor networks. IEEE Trans Inf Forensics Secur 5(3):518–533CrossRef

Rajasegarar S, Bezdek JC, Leckie C, Palaniswami M (January 2010) Elliptical anomalies in wireless sensor networks. ACM Trans. Sen. Netw. vol. 6, pp. 7:1–7:28. [Online]. Available: http://doi.acm.org/10.1145/1653760.1653767

Rajasegarar S, Bezdek J, Leckie C, Palaniswami M (oct 2007) Analysis of anomalies in ibrl data from a wireless sensor network deployment. In: Sensor technologies and applications, 2007. SensorComm 2007. tnternational conference on, pp 158–163

Rajasegarar S, Bezdek J, Moshtaghi M, Leckie C, Havens T, Palaniswami M (2012) Measures for clustering and anomaly detection in sets of higher dimensional ellipsoids. In: Neural networks (IJCNN), The 2012 international joint conference on. IEEE, pp 1–8

Rajasegarar S, Leckie C, Palaniswami M (may 2008) Cesvm: centered hyperellipsoidal support vector machine based anomaly detection. In: Communications, 2008. ICC ’08. IEEE international conference on, pp 1610–1614

Rajasegarar S, Leckie C, Palaniswami M, Bezdek J (june 2007) Quarter sphere based distributed anomaly detection in wireless sensor networks. In: IEEE international conference on communications, 2007. ICC ’07, pp 3864–3869

Rajasegarar S, Leckie C, Palaniswami M, Bezdek JC (oct 2006) Distributed anomaly detection in wireless sensor networks. In: Communication systems, 2006. ICCS 2006. 10th IEEE Singapore International Conference on pp 1–5

Ross G, Tasoulis D, Adams N (2009) Online annotation and prediction for regime switching data streams. In: Proceedings of the 2009 ACM symposium on applied computing. ACM, pp 1501–1505

Saada M, Meng Q (2012) An efficient algorithm for anomaly detection in a flight system using dynamic bayesian networks. In: Neural information processing. Springer, pp 620–628

Schieferdecker D, Völker M, Wagner D (2011) Efficient algorithms for distributed detection of holes and boundaries in wireless networks. Experimental Algorithms, pp 388–399

Scholkopf B, Smola AJ (2001) Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT Press, Cambridge

Schölkopf B, Platt JC, Shawe-Taylor JC, Smola AJ, Williamson RC (July 2001) Estimating the support of a high-dimensional distribution, Neural Comput., vol. 13, pp. 1443–1471. [Online]. Available: http://portal.acm.org/citation.cfm?id=1119748.1119749

Schwenker F (2000) Hierarchical support vector machines for multi-class pattern recognition. In: Knowledge-based intelligent engineering systems and allied technologies, 2000. Proceedings. Fourth international conference on, vol. 2. IEEE, pp 561–565

Shahid N, Naqvi IH (2011) Energy efficient outlier detection in wsns based on temporal and attribute correlations. In: International conference on emerging technologies

Shahid N, Naqvi IH, Qaisar SB, (2012) Quarter-Sphere SVM: attribute and Spatio-Temporal correlations based outlier & event detection in wireless sensor networks. In, (2012) IEEE wireless communications and networking conference: Mobile and Wireless Networks (IEEE WCNC 2012 Track 3 Mobile & Wireless). France, Apr, Paris

Shahid N, Naqvi IH, Qaisar SB (Nov 2012) Real time energy efficient approach to outlier & event detection in wireless sensor networks. In: 13th IEEE international conference on communication systems (2012) IEEE ICCS’12. Singapore, Singapore

Shahid N, Naqvi I, Qaisar S (2012) Characteristics and classification of outlier detection techniques for wireless sensor networks in harsh environments: a survey. Artif Intell Rev, pp 1–36. [Online]. Available: http://dx.doi.org/10.1007/s10462-012-9370-y

Sharma A, Golubchik L, Govindan R (2010) Sensor faults: detection methods and prevalence in real-world datasets. ACM Trans Sens Netw (TOSN) 6(3):23

Sheng B, Li Q, Mao W, jin W (2007) Outlier detection in sensor networks

Smola A, Schölkopf B (1998) Learning with kernels. Citeseer.

Stankovic S, Rakocevic G, Kojic N, Milicev D (2012) A classification and comparison of data mining algorithms for wireless sensor networks. In: Industrial Technology (ICIT), 2012 IEEE international conference on. IEEE, pp 265–270

Steinwart I, Christmann A (2008) Support vector machines (information science & statistics). Recherche 67:02

Subramaniam S, Palpanas T, Papadopoulos D, Kalogeraki V, Gunopulos D (2006) Online outlier detection in sensor data using non-parametric models. In: Proceedings of the 32nd international conference on very large data bases, ser. VLDB ’06. VLDB Endowment, pp 187–198. [Online]. Available: http://portal.acm.org/citation.cfm?id=1182635.1164145

Suthaharan S (2012) An iterative ellipsoid-based anomaly detection technique for intrusion detection systems. In: Southeastcon, 2012 Proceedings of IEEE. IEEE, pp 1–6

Suthaharan S, Alzahrani M, Rajasegarar S, Leckie C, Palaniswami M (dec 2010) Labelled data collection for anomaly detection in wireless sensor networks. In: Intelligent sensors, sensor networks and information processing (ISSNIP), 2010 sixth international conference on. pp 269–274

Suthaharan S, Leckie C, Moshtaghi M, Karunasekera S, Rajasegarar S (2010) Sensor data boundary estimation for anomaly detection in wireless sensor networks. In: Mobile Adhoc and sensor systems (MASS), 2010 IEEE 7th international conference on. IEEE, pp 546–551

Tan P, Steinback M, Kumar V (2006) Introduction to data mining. Addison Wesley, Reading

Tax DMJ, Duin RPW (1999) Data domain description using support vectors. In: ESANN’99, pp 251–256

Tax D, Duin R (2004) Support vector data description. Mach Learn 54(1):45–66CrossRefMATH

Trinidad M, Valle M, Toledo C (2012) Indoors and outdoors event detection for embedded wireless sensors. Embedded systems and wireless technology, theory and practical applications, p 139

Tutorial on Wireless Communications and Electronic Tracking (2009)

Wang D, Yeung DS, Tsang ECC (2006) Structured one-class classification. IEEE Trans Syst Man Cybern Part B Cybern 36(6):1283–1295CrossRef

Weston J, Watkins C (1999) Support vector machines for multi-class pattern recognition. Proc seventh Eur Symp Artif Neural Netw 4(6):219–224

Weston J, Watkins C (1998) Multi-class support vector machines. Citeseer, Tech. Rep

Wu W, Cheng X, Ding M, Xing K, Liu F, Deng P (2007) Localized outlying and boundary data detection in sensor networks. IEEE Trans Knowl Data Eng 19(8):1145–1157CrossRef

Xie M, Hu J, Han S, Chen H (2012) Scalable hyper-grid k-nn-based online anomaly detection in wireless sensor networks

Xie M, Hu J, Tian B (2012) Histogram-based online anomaly detection in hierarchical wireless sensor networks. In: Trust, security and privacy in computing and communications (TrustCom), 2012 IEEE 11th international conference on. IEEE, pp 751–759

Xu T (2009) A new sphere-structure multi-class classifier. In: Circuits, communications and systems, 2009. PACCS’09. Pacific-Asia Conference on. IEEE, pp 520–525

Xue W, Luo Q, Chen L, Liu Y (2006) Contour map matching for event detection in sensor networks. In: Proceedings of the 2006 ACM SIGMOD international conference on Management of data, ser. SIGMOD ’06. New York, NY, USA: ACM, pp. 145–156. [Online]. Available: http://doi.acm.org/10.1145/1142473.1142491

Xu T, He D, Luo Y (2007) A new orientation for multi-class svm. In: Software engineering, artificial intelligence, networking, and parallel/distributed computing, 2007. SNPD 2007. Eighth ACIS International Conference on, vol. 3. IEEE, pp 899–904

Yang Z, Meratnia N, Havinga P (dec 2008) An online outlier detection technique for wireless sensor networks using unsupervised quarter-sphere support vector machine. In: Intelligent sensors, sensor networks and information processing, 2008. ISSNIP 2008. International conference on, pp 151–156

Yeung D, Wang D, Ng W, Tsang E, Wang X (2007) Structured large margin machines: sensitive to data distributions. Mach learn 68(2):171–200CrossRef

Yozo CP, Hida Y, Huang P, Nishtala R (2004) Aggregation query under uncertainty in sensor networks. Tech, Rep

Zhang Y (June 2010) Observing the unobservable - distributed online outlier detection in wireless sensor networks. University of Twente, Ph.D. dissertation

Zhang Y, Meratnia N, Havinga P (2010) Outlier detection techniques for wireless sensor networks: a survey. IEEE Commun Surv Tutor 12(2):159–170CrossRef

Zhang X, Gu C, Lin J (2006) Support vector machines for anomaly detection. In: Intelligent control and automation, 2006. WCICA 2006. The sixth world congress on, vol. 1. IEEE, pp 2594–2598

Zhang Y, Hamm N, Meratnia N, Stein A, van de Voort M, Havinga P (2012) Statistics-based outlier detection for wireless sensor networks

Zhang Y, Meratnia N, Havinga P (2009) Adaptive and online one-class support vector machine-based outlier detection techniques for wireless sensor networks. In: Proceedings international confernce advanced information networking and applications workshops WAINA ’09, pp 990–995

Zhang Y, Meratnia N, Havinga P (2012) Distributed online outlier detection in wireless sensor networks using ellipsoidal support vector machine, Ad Hoc Networks

Zhang Y, Meratnia N, Havinga PJM (July 2009) Hyperellipsoidal svm-based outlier detection technique for geosensor networks. In: Third international conference on Geosensor Networks, Oxford, UK, ser. Lecture Notes in Computer Science, vol. 5659. Berlin: Springer, pp 31–41

Zhang Y, Meratnia N, Havinga PJM (November 2007) A taxonomy framework for unsupervised outlier detection techniques for multi-type data sets, http://eprints.eemcs.utwente.nl/11366/, Centre for Telematics and Information Technology University of Twente, Enschede, Technical Report TR-CTIT-07-79

Zhang K, Shi S, Gao H, Li J (2007) Unsupervised outlier detection in sensor networks using aggregation tree. In: Proceedings of the 3rd international conference on advanced data mining and applications, ser. ADMA ’07. Berlin, Heidelberg: Springer, pp 158–169. [Online]. Available: http://dx.doi.org/10.1007/978-3-540-73871-816

Zhuang Y, Chen L (2006) In-network outlier cleaning for data collection in sensor networks. In: In CleanDB, workshop in VLDB. APPENDIX, pp 41–48

Zhu M, Wang Y, Chen S, Liu X (2003) Sphere-structured support vector machines for multi-class pattern recognition. Rough Sets, Fuzzy Sets, Data Mining, and Granular, Computing, pp 589–589

Zoumboulakis M, Roussos G (2007) Escalation: complex event detection in wireless sensor networks. In: Smart sensing and, context, pp 270–285

Titel: One-class support vector machines: analysis of outlier detection for wireless sensor networks in harsh environments
verfasst von: Nauman Shahid
Ijaz Haider Naqvi
Saad Bin Qaisar
Publikationsdatum: 01.04.2015
Verlag: Springer Netherlands
Erschienen in: Artificial Intelligence Review / Ausgabe 4/2015
Print ISSN: 0269-2821
Elektronische ISSN: 1573-7462
DOI: https://doi.org/10.1007/s10462-013-9395-x

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Weitere Artikel der Ausgabe 4/2015

Research on the hybrid models of granular computing and support vector machine

Context-based global multi-class semantic image segmentation by wireless multimedia sensor networks

Computational irony: A survey and new perspectives

Validation of knowledge-based systems: a reassessment of the field

Research on data stream clustering algorithms

RETRACTED ARTICLE: From the human visual system to the computational models of visual attention: a survey

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