Performance analysis of intrusion detection systems using the PyCaret machine learning library on the UNSW-NB15 dataset
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
| Główni autorzy: | , , , |
|---|---|
| Kolejni autorzy: | |
| Format: | Praca dyplomowa |
| Język: | English |
| Wydane: |
Brac University
2021
|
| Hasła przedmiotowe: | |
| Dostęp online: | http://hdl.handle.net/10361/15701 |
| id |
10361-15701 |
|---|---|
| record_format |
dspace |
| spelling |
10361-157012022-01-26T10:20:02Z Performance analysis of intrusion detection systems using the PyCaret machine learning library on the UNSW-NB15 dataset Abdullah Iqbal, Faisal Bin Biswas, Srijon Urba, Rubabatul Chakrabarty, Amitabha Department of Computer Science and Engineering, Brac University IoT Anomaly detection In- trusion detection PyCaret UNSW-NB15 Internet of Things Machine learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 36-39). As one of the fastest growing technologies on earth, the Internet of Things (IoT) is being embraced almost everywhere. From smart home technology to industrial automation, IoT is revolutionizing almost everything around us. It has enabled humans and organizations to do more with less, both in terms of time, as well as - nances. This feat of the Internet of Things, however, has also led to an alarming rise in attacks on IoT networks. Among these attacks, botnet intrusions are perhaps the most worrying ones. And with the advancement of time and technology, attackers are getting more creative. Hence, it is important to use better and more e cient machine learning technologies to identify these attacks and detect these intrusions before they can paralyze the system. This research aims to identify a more e cient machine learning approach for detecting botnets in IoT networks by utilizing the Py- Caret machine learning library and analyzing its overall performance. The research will encompass di erent classi ers and analyze the di erent performance metrics for each of them. It will also shed light on the feasibility of using the PyCaret library and how well suited it is for such usage. Abdullah Faisal Bin Iqbal Srijon Biswas Rubabatul Urba B. Computer Science 2021-12-06T08:01:38Z 2021-12-06T08:01:38Z 2021 2021-06 Thesis ID 16101305 ID 17101339 ID 21141053 ID 17101426 http://hdl.handle.net/10361/15701 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. 39 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
IoT Anomaly detection In- trusion detection PyCaret UNSW-NB15 Internet of Things Machine learning |
| spellingShingle |
IoT Anomaly detection In- trusion detection PyCaret UNSW-NB15 Internet of Things Machine learning Abdullah Iqbal, Faisal Bin Biswas, Srijon Urba, Rubabatul Performance analysis of intrusion detection systems using the PyCaret machine learning library on the UNSW-NB15 dataset |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. |
| author2 |
Chakrabarty, Amitabha |
| author_facet |
Chakrabarty, Amitabha Abdullah Iqbal, Faisal Bin Biswas, Srijon Urba, Rubabatul |
| format |
Thesis |
| author |
Abdullah Iqbal, Faisal Bin Biswas, Srijon Urba, Rubabatul |
| author_sort |
Abdullah |
| title |
Performance analysis of intrusion detection systems using the PyCaret machine learning library on the UNSW-NB15 dataset |
| title_short |
Performance analysis of intrusion detection systems using the PyCaret machine learning library on the UNSW-NB15 dataset |
| title_full |
Performance analysis of intrusion detection systems using the PyCaret machine learning library on the UNSW-NB15 dataset |
| title_fullStr |
Performance analysis of intrusion detection systems using the PyCaret machine learning library on the UNSW-NB15 dataset |
| title_full_unstemmed |
Performance analysis of intrusion detection systems using the PyCaret machine learning library on the UNSW-NB15 dataset |
| title_sort |
performance analysis of intrusion detection systems using the pycaret machine learning library on the unsw-nb15 dataset |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/15701 |
| work_keys_str_mv |
AT abdullah performanceanalysisofintrusiondetectionsystemsusingthepycaretmachinelearninglibraryontheunswnb15dataset AT iqbalfaisalbin performanceanalysisofintrusiondetectionsystemsusingthepycaretmachinelearninglibraryontheunswnb15dataset AT biswassrijon performanceanalysisofintrusiondetectionsystemsusingthepycaretmachinelearninglibraryontheunswnb15dataset AT urbarubabatul performanceanalysisofintrusiondetectionsystemsusingthepycaretmachinelearninglibraryontheunswnb15dataset |
| _version_ |
1814309124585816064 |