Intrusion of Malware (DDoS) detection in IoT devices using Machine Learning on Cyberspace
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.
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Brac University
2022
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| Онлайн доступ: | http://hdl.handle.net/10361/17018 |
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10361-170182022-07-17T21:01:34Z Intrusion of Malware (DDoS) detection in IoT devices using Machine Learning on Cyberspace Al Amin, Istiak Lamiya, Salsabil Sheikh, Noshin Anjum Haque, S. M. Tanjimul Arif, Hossain Department of Computer Science and Engineering, Brac University IoT DDoS k-Nearest-Neighbour Random Forest Naive Bayes Artificial Neural Network Support Vector Machine Cyberspace 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, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 36-37). Nowadays, the number of interconnected devices (IoT) is increasing dramatically. This expansion poses new security problems for network operators, IoT service providers, and users. Security measures implemented on IoT devices are getting complex due to their heterogeneity and constraints. Attackers have utilized IoT devices to execute massive attacks like DDoS, Zero-Day-Exploitation, Ransomware, etc. The most significant measure to safeguard services from insecure IoT devices is to increase security consciousness in the core network. On the other hand, this thesis suggests a machine learning DDoS detection and diminution technique. The proposed approach was assessed by applying five supervised machine learning classification methods. The evaluation findings reveal that k-NN and Random Forest algorithms outperform ANN, SVM, and Naïve Bayes algorithms. Consequently, the findings of this study can assist academics in further research on malware detection systems for IoT devices. Istiak Al Amin Salsabil Lamiya Noshin Anjum Sheikh S. M. Tanjimul Haque B. Computer Science 2022-07-17T08:36:41Z 2022-07-17T08:36:41Z 2022 2022-01 Thesis ID: 17201025 ID: 17201115 ID: 17201114 ID: 17301095 http://hdl.handle.net/10361/17018 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. 37 Pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
en_US |
| topic |
IoT DDoS k-Nearest-Neighbour Random Forest Naive Bayes Artificial Neural Network Support Vector Machine Cyberspace Internet of things Machine learning |
| spellingShingle |
IoT DDoS k-Nearest-Neighbour Random Forest Naive Bayes Artificial Neural Network Support Vector Machine Cyberspace Internet of things Machine learning Al Amin, Istiak Lamiya, Salsabil Sheikh, Noshin Anjum Haque, S. M. Tanjimul Intrusion of Malware (DDoS) detection in IoT devices using Machine Learning on Cyberspace |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. |
| author2 |
Arif, Hossain |
| author_facet |
Arif, Hossain Al Amin, Istiak Lamiya, Salsabil Sheikh, Noshin Anjum Haque, S. M. Tanjimul |
| format |
Thesis |
| author |
Al Amin, Istiak Lamiya, Salsabil Sheikh, Noshin Anjum Haque, S. M. Tanjimul |
| author_sort |
Al Amin, Istiak |
| title |
Intrusion of Malware (DDoS) detection in IoT devices using Machine Learning on Cyberspace |
| title_short |
Intrusion of Malware (DDoS) detection in IoT devices using Machine Learning on Cyberspace |
| title_full |
Intrusion of Malware (DDoS) detection in IoT devices using Machine Learning on Cyberspace |
| title_fullStr |
Intrusion of Malware (DDoS) detection in IoT devices using Machine Learning on Cyberspace |
| title_full_unstemmed |
Intrusion of Malware (DDoS) detection in IoT devices using Machine Learning on Cyberspace |
| title_sort |
intrusion of malware (ddos) detection in iot devices using machine learning on cyberspace |
| publisher |
Brac University |
| publishDate |
2022 |
| url |
http://hdl.handle.net/10361/17018 |
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