Constructing IoT botnet detection model based on degree centrality and path analysis

• Main Author: Wan Mohd Zaki, Wan Nur Fatihah
• Format: Thesis
• Language: English; English
• Published: 2023
• Subjects: T Technology (General); TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utem.edu.my/id/eprint/27729/1/Constructing%20IoT%20botnet%20detection%20model%20based%20on%20degree%20centrality%20and%20path%20analysis.pdf; http://eprints.utem.edu.my/id/eprint/27729/2/Constructing%20IoT%20botnet%20detection%20model%20based%20on%20degree%20centrality%20and%20path%20analysis.pdf; http://eprints.utem.edu.my/id/eprint/27729/; https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=123731

Internet of things (IoT) Botnet is a network of connected devices, generally smart devices with software and intelligent sensors, networked over the internet to send and receive data from other intelligent devices infected with IoT Botnet malware. The development of IoT Botnet in IoT devices has a significant impact on network security. IoT Botnet attack activities have become a major problem to mitigate since IoT Botnet is the most recent and high-profile security issue. IoT Botnet activities is challenging task in order to identify since IoT Botnet are targeting IoT devices. In addition, the current IoT Botnet detection is still not have ability to reveal patterns of IoT Botnet attacks and ignore the important recognization of IoT Botnet behaviors has resulted loss of meet the detection criteria. Thus, the focus of this research is to identify IoT Botnet behaviour, to propose an IoT Botnet attack pattern based on its behaviour, to construct an IoT Botnet detection model and to validate the selection of the IoT Botnet detection model utilising detection of the IoT Botnet attack detection criteria. In order to deal with this problem, the research methodology is essential to ensure the research is appropriately implemented by providing a systematic organization with the appropriate guideline. This research have five phases of research methodology which are study and requirement analysis, data collection, analysis and design, developing the new model and validation and testing. Furthermore, this research is constructing the IoT Botnet attack pattern based on combining the IoT Botnet life cycle and IoT Botnet behaviour through the IoT Botnet activities. Then, this research has develop IoT Botnet detection model based on graph analytics approach respectively to detect IoT Botnet attack activities. The earlier detection of IoT Botnet has been visualized by IoT Botnet attack patterns using the degree centrality and path analysis. In validation process, the result showed that the proposed IoT Botnets model has accomplished all the selection detection criterias. Therefore, it is necessary for this research to constructing IoT Botnet detection model based on degree centrality and path analysis.