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Anomaly Detection using Hadoop and MapReduce Technique in Cloud with Sensor Data

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2015
Ihab I.M. Alghussein, Walid Mohamed Aly, Mohamad Abou El-Nasr

Ihab I M Alghussein, Walid Mohamed Aly and Mohamad Abou El-Nasr. Article: Anomaly Detection using Hadoop and MapReduce Technique in Cloud with Sensor Data. International Journal of Computer Applications 125(1):22-26, September 2015. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Ihab I.M. Alghussein and Walid Mohamed Aly and Mohamad Abou El-Nasr},
	title = {Article: Anomaly Detection using Hadoop and MapReduce Technique in Cloud with Sensor Data},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {125},
	number = {1},
	pages = {22-26},
	month = {September},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


This paper presents a model to observation the Cloud computing for any anomalous activity. Hadoop it is a largely used open source Cloud Computing framework to huge data. It uses the model Machine Learning technique to detect classify anomalies of sensory observation and help to in ensuring the stabilization of virtual sensor networks. The framework it’s built on top of the Hadoop and MapReduce implementation which is use one of the Machines Learning techniques to detect these anomalies. Preliminary results show that our classification mechanism is promising and able to detect anomalous events that may cause a threat to the Cloud Computing.


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MapReduce, Hadoop, anomaly detection, Machine Learning, Cloud Computing.