Botnet detection In IoT devices using machine learning
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.
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Brac University
2020
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| Länkar: | http://hdl.handle.net/10361/14069 |
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10361-140692022-01-26T10:04:51Z Botnet detection In IoT devices using machine learning Rouf, Shakir Akash, Nazmus Sakib Chowdhury, Amlan Jahan, Sigma Chakrabarty, Dr. Amitabha Department of Computer Science and Engineering, Brac University Botnet DDoS (Distributed Denial of Service) IoT (Internet of Things) Machine Learning Classifiers 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 46-49). Internet of Things (IoT) devices are a group of interconnected devices or machines that have the ability to transfer data over a network without the influence of any external factor. The technology makes use of sensor nodes embedded into everyday computing objects, which communicate in a wireless multi-hop fashion to exchange data over a local network or the internet. With the rapid technological advancements taking place around the globe, the use of IoT devices has also increased proportionately. Although the prevalence of IoT devices in human lives has influenced the IoT manufacturers to make it cheap an accessible, but on the other hand, the system provides minimal control with no substantial security measures due to its prodigious application, which in turn makes it susceptible to botnet attacks. Botnet is a network of interconnected malware contaminated IoT devices, individually referred to as a bot. These bots are used as instruments of malicious attack on a network of IoT devices which allows the group of hackers (referred to as Botmaster) to perform distributed denial-of-service attack (DDoS), data theft and spam by flooding the network with unnecessary information. As a result, botnet detection has risen as an essential ingredient of network security. In this paper, our motive is to use various Machine Learning algorithms to detect botnet attacks and filter out the algorithm which will be most suitable and accurate to detect such attacks by comparing the derived outputs. Shakir Rouf Nazmus Sakib Akash Amlan Chowdhury Sigma Jahan B. Computer Science 2020-10-27T05:48:08Z 2020-10-27T05:48:08Z 2019 2019-12 Thesis ID: 16101104 ID: 16101208 ID: 16101042 ID: 16301031 http://hdl.handle.net/10361/14069 en_US 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. 49 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
en_US |
| topic |
Botnet DDoS (Distributed Denial of Service) IoT (Internet of Things) Machine Learning Classifiers |
| spellingShingle |
Botnet DDoS (Distributed Denial of Service) IoT (Internet of Things) Machine Learning Classifiers Rouf, Shakir Akash, Nazmus Sakib Chowdhury, Amlan Jahan, Sigma Botnet detection In IoT devices using machine learning |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019. |
| author2 |
Chakrabarty, Dr. Amitabha |
| author_facet |
Chakrabarty, Dr. Amitabha Rouf, Shakir Akash, Nazmus Sakib Chowdhury, Amlan Jahan, Sigma |
| format |
Thesis |
| author |
Rouf, Shakir Akash, Nazmus Sakib Chowdhury, Amlan Jahan, Sigma |
| author_sort |
Rouf, Shakir |
| title |
Botnet detection In IoT devices using machine learning |
| title_short |
Botnet detection In IoT devices using machine learning |
| title_full |
Botnet detection In IoT devices using machine learning |
| title_fullStr |
Botnet detection In IoT devices using machine learning |
| title_full_unstemmed |
Botnet detection In IoT devices using machine learning |
| title_sort |
botnet detection in iot devices using machine learning |
| publisher |
Brac University |
| publishDate |
2020 |
| url |
http://hdl.handle.net/10361/14069 |
| work_keys_str_mv |
AT roufshakir botnetdetectioniniotdevicesusingmachinelearning AT akashnazmussakib botnetdetectioniniotdevicesusingmachinelearning AT chowdhuryamlan botnetdetectioniniotdevicesusingmachinelearning AT jahansigma botnetdetectioniniotdevicesusingmachinelearning |
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