Anomaly detection In IoT using machine learning algorithms

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.

Bibliographic Details
Main Authors:	Arko, Aritro Roy, Khan, Saadat Hasan, Preety, A a Anjum, Biswas, Mehrab Hossain
Other Authors:	Chakrabarty, Amitabha
Format:	Thesis
Language:	English
Published:	Brac University 2019
Subjects:	IoT (Internet of Things) Anomaly detection Intrusion detection Machine learning algorithms Sensor data Network data UNSW-NB15 Machine learning Internet of things Intrusion detection systems (Computer security) Anomaly detection (Computer security)
Online Access:	http://hdl.handle.net/10361/12776

id	10361-12776
record_format	dspace
spelling	10361-127762024-08-21T05:22:31Z Anomaly detection In IoT using machine learning algorithms Arko, Aritro Roy Khan, Saadat Hasan Preety, A a Anjum Biswas, Mehrab Hossain Chakrabarty, Amitabha Department of Computer Science and Engineering, Brac University IoT (Internet of Things) Anomaly detection Intrusion detection Machine learning algorithms Sensor data Network data UNSW-NB15 Machine learning Internet of things Intrusion detection systems (Computer security) Anomaly detection (Computer security) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019. Cataloged from PDF version of thesis. Includes bibliographical references (pages 43-45). Internet of Things (IoT) is growing as one of the fastest developing technologies around the world. With IPv6 settling down, people have a lot of addressing spaces left that even allows sensors to communicate with each other while collecting data, leaving alone cars that communicate while travelling. IoT (Internet of Things) has changed how humans, machines and devices communicate with one another. However, with its growth, a very alarming topic is the security and privacy issues that are encountered regularly. As many devices exchange their data through internet, there is a high possibility that a device may be attacked with a malicious packet of data. In such cases, the security of the network of communication should be strong enough to identify malicious data. In other words, it is very important to create an intrusion detection system for the network. In our research, we propose a comparison between di erent machine learning algorithms that can be used to identify any malicious or anomalous data and provide the best algorithm for two data-sets. One dataset is on the environmental characteristics collected from sensors and another one is network dataset. The rst data-set is developed from the data exchanged between the sensors in an IoT environment and the second dataset is UNSW-NB15 data which is available online. Aritro Roy Arko Saadat Hasan Khan A a Anjum Preety Mehrab Hossain Biswas B.Sc in Computer Science 2019-10-02T08:34:38Z 2019-10-02T08:34:38Z 2019 2019-08 Thesis ID 15201020 ID 15201013 ID 15301046 ID 15201008 http://hdl.handle.net/10361/12776 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 45 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	IoT (Internet of Things) Anomaly detection Intrusion detection Machine learning algorithms Sensor data Network data UNSW-NB15 Machine learning Internet of things Intrusion detection systems (Computer security) Anomaly detection (Computer security)
spellingShingle	IoT (Internet of Things) Anomaly detection Intrusion detection Machine learning algorithms Sensor data Network data UNSW-NB15 Machine learning Internet of things Intrusion detection systems (Computer security) Anomaly detection (Computer security) Arko, Aritro Roy Khan, Saadat Hasan Preety, A a Anjum Biswas, Mehrab Hossain Anomaly detection In IoT using machine learning algorithms
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.
author2	Chakrabarty, Amitabha
author_facet	Chakrabarty, Amitabha Arko, Aritro Roy Khan, Saadat Hasan Preety, A a Anjum Biswas, Mehrab Hossain
format	Thesis
author	Arko, Aritro Roy Khan, Saadat Hasan Preety, A a Anjum Biswas, Mehrab Hossain
author_sort	Arko, Aritro Roy
title	Anomaly detection In IoT using machine learning algorithms
title_short	Anomaly detection In IoT using machine learning algorithms
title_full	Anomaly detection In IoT using machine learning algorithms
title_fullStr	Anomaly detection In IoT using machine learning algorithms
title_full_unstemmed	Anomaly detection In IoT using machine learning algorithms
title_sort	anomaly detection in iot using machine learning algorithms
publisher	Brac University
publishDate	2019
url	http://hdl.handle.net/10361/12776
work_keys_str_mv	AT arkoaritroroy anomalydetectioniniotusingmachinelearningalgorithms AT khansaadathasan anomalydetectioniniotusingmachinelearningalgorithms AT preetyaaanjum anomalydetectioniniotusingmachinelearningalgorithms AT biswasmehrabhossain anomalydetectioniniotusingmachinelearningalgorithms
_version_	1814307634360090624

Anomaly detection In IoT using machine learning algorithms

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