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Tampering Classification Using Accelerometer Data

Lindholm, Mikael LU and Lindgren, Linus LU (2017) In Master's Theses in Mathematical Sciences FMA820 20171
Mathematics (Faculty of Engineering)
Abstract
Network Video Door Stations are IP-based door stations for two-way communication, identification, and remote entry control. They have a number of different sensors e.g video, audio and accelerometer that can measure external data. These door stations serve multiple purposes including that of acting as a security feature and they are often exposed to malicious intent. The goal of this master thesis is to utilize machine learning techniques to classify tampering events on the Axis A8105-VE using a three dimensional low cost MEMS accelerometer. A reliable system using support vector machine was developed and tested on the Axis A8105-VE. The classification scheme developed achieved an average accuracy of 99.8% with a response time of 1.624... (More)
Network Video Door Stations are IP-based door stations for two-way communication, identification, and remote entry control. They have a number of different sensors e.g video, audio and accelerometer that can measure external data. These door stations serve multiple purposes including that of acting as a security feature and they are often exposed to malicious intent. The goal of this master thesis is to utilize machine learning techniques to classify tampering events on the Axis A8105-VE using a three dimensional low cost MEMS accelerometer. A reliable system using support vector machine was developed and tested on the Axis A8105-VE. The classification scheme developed achieved an average accuracy of 99.8% with a response time of 1.624 seconds. The data used in this thesis contains 2119 observations introducing 13 different environments. The feature vector used in the binary classification consists of 41 features focusing on probabilistic-, periodic-, frequency- and generic measurements of the time-series signal based on the accelerometer data. (Less)
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author
Lindholm, Mikael LU and Lindgren, Linus LU
supervisor
organization
alternative title
Tampering Classification Using Machine Learning
course
FMA820 20171
year
type
H2 - Master's Degree (Two Years)
subject
keywords
machine learning, classification, accelerometer, support vector machine, doorstation, tampering detection
publication/series
Master's Theses in Mathematical Sciences
report number
LUFTMA-3319-2017
ISSN
1404-6342
other publication id
2017:E27
language
English
id
8913973
date added to LUP
2017-06-12 15:04:42
date last changed
2017-06-12 15:04:42
@misc{8913973,
  abstract     = {Network Video Door Stations are IP-based door stations for two-way communication, identification, and remote entry control. They have a number of different sensors e.g video, audio and accelerometer that can measure external data. These door stations serve multiple purposes including that of acting as a security feature and they are often exposed to malicious intent. The goal of this master thesis is to utilize machine learning techniques to classify tampering events on the Axis A8105-VE using a three dimensional low cost MEMS accelerometer. A reliable system using support vector machine was developed and tested on the Axis A8105-VE. The classification scheme developed achieved an average accuracy of 99.8% with a response time of 1.624 seconds. The data used in this thesis contains 2119 observations introducing 13 different environments. The feature vector used in the binary classification consists of 41 features focusing on probabilistic-, periodic-, frequency- and generic measurements of the time-series signal based on the accelerometer data.},
  author       = {Lindholm, Mikael and Lindgren, Linus},
  issn         = {1404-6342},
  keyword      = {machine learning,classification,accelerometer,support vector machine,doorstation,tampering detection},
  language     = {eng},
  note         = {Student Paper},
  series       = {Master's Theses in Mathematical Sciences},
  title        = {Tampering Classification Using Accelerometer Data},
  year         = {2017},
}