Anomaly detection in wireless sensor networks

Wireless Sensor Network can be defined as a network of integrated sensors responsible for environmental sensing, data processing and communication with other sensors and the base station while consuming low power. Today, WSNs are being used in almost every part of life. The cost effective nature of...

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Main Author: Lateef, Asim
Other Authors: Informaatioteknologian tiedekunta, Faculty of Information Technology, Tietotekniikan laitos, Department of Mathematical Information Technology, University of Jyväskylä, Jyväskylän yliopisto
Format: Master's thesis
Language:eng
Published: 2016
Subjects:
Online Access: https://jyx.jyu.fi/handle/123456789/52511
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author Lateef, Asim
author2 Informaatioteknologian tiedekunta Faculty of Information Technology Tietotekniikan laitos Department of Mathematical Information Technology University of Jyväskylä Jyväskylän yliopisto
author_facet Lateef, Asim Informaatioteknologian tiedekunta Faculty of Information Technology Tietotekniikan laitos Department of Mathematical Information Technology University of Jyväskylä Jyväskylän yliopisto Lateef, Asim Informaatioteknologian tiedekunta Faculty of Information Technology Tietotekniikan laitos Department of Mathematical Information Technology University of Jyväskylä Jyväskylän yliopisto
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description Wireless Sensor Network can be defined as a network of integrated sensors responsible for environmental sensing, data processing and communication with other sensors and the base station while consuming low power. Today, WSNs are being used in almost every part of life. The cost effective nature of WSNs is beneficial for environmental monitoring, production facilities and security monitoring. At the same time WSNs are vulnerable to security breaches, attacks and information leakage. Anomaly detection techniques are used to detect such activities over the network that do not conform to the normal behavior of the network communication. Supervised Machine learning approach is one way to detect anomalies where a normal model is developed with known responses called labels and this model is tested against new data sets. We experimented Supervised Machine Learning approach for the labelled sensor data set of Humidity and Temperature and the results show that KNN (K Nearest Neighbor) proves to be the best anomaly detection algorithm for this data set.
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spellingShingle Lateef, Asim Anomaly detection in wireless sensor networks Wireless sensor networks anomaly detection supervised machine learning Tietotekniikka Mathematical Information Technology 602 koneoppiminen sensoriverkot
title Anomaly detection in wireless sensor networks
title_full Anomaly detection in wireless sensor networks
title_fullStr Anomaly detection in wireless sensor networks Anomaly detection in wireless sensor networks
title_full_unstemmed Anomaly detection in wireless sensor networks Anomaly detection in wireless sensor networks
title_short Anomaly detection in wireless sensor networks
title_sort anomaly detection in wireless sensor networks
title_txtP Anomaly detection in wireless sensor networks
topic Wireless sensor networks anomaly detection supervised machine learning Tietotekniikka Mathematical Information Technology 602 koneoppiminen sensoriverkot
topic_facet 602 Mathematical Information Technology Tietotekniikka Wireless sensor networks anomaly detection koneoppiminen sensoriverkot supervised machine learning
url https://jyx.jyu.fi/handle/123456789/52511 http://www.urn.fi/URN:NBN:fi:jyu-201612215230
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