Tampering Classification Using Accelerometer Data
(2017) In Master's Theses in Mathematical Sciences FMA820 20171Mathematics (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)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8913973
- author
- Lindholm, Mikael LU and Lindgren, Linus LU
- supervisor
-
- Karl Åström LU
- organization
- alternative title
- Tampering Classification Using Machine Learning
- course
- FMA820 20171
- year
- 2017
- 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}}, language = {{eng}}, note = {{Student Paper}}, series = {{Master's Theses in Mathematical Sciences}}, title = {{Tampering Classification Using Accelerometer Data}}, year = {{2017}}, }