Enhancing IoT Device Security a Hybrid Machine Learning-Based Approach Leveraging K-Means Clustering for Intrusion Detection

Ike Mgbeafulike and Ihediuche Evangeline Ndidi
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Ike Mgbeafulike: Department of Computer Science, Chukwuemeka Odumegwu Ojukwu University, Uli AN, NG
Ihediuche Evangeline Ndidi: Dennis Memorial Grammar school Onitsha Anambra State

International Journal of Research and Innovation in Applied Science, 2024, vol. 9, issue 11, 38-50

Abstract: Internet of Things (IoT) is the interconnection of heterogeneous smart devices through the Internet with diverse application areas. The huge number of smart devices and the complexity of networks has made it impossible to secure the data and communication between devices. Various conventional security controls are insufficient to prevent numerous attacks against these information-rich devices. Along with enhancing existing approaches, a peripheral defence, Intrusion Detection System (IDS) using machine learning and k-means clustering mode proved efficient in most scenarios. To do this, a hybrid security framework system was proposed and its features defined in the introductory chapter of this research. Literature review was conducted on what has been done by other researchers in the field of internet/network security and k-means clustering model. The design phase of the research was done where the blueprint that would be used to design the system was described and the implementation of the developed system was presented. The methodology adopted in this research is Object Oriented Analysis and Design Methodology. The hybrid security framework system using machine learning and k-means clustering model was designed using the combination of the Visual Basic Object-Oriented Programming Language and the SQLite relational database system. The system was designed to foster privacy preservation using a collaborative defense mechanism in the IoT ecosystem. The new system will enable the network to be protected and secure by granting access to authenticated devices, employing the use of machine learning to filter out malicious traffic and allowing the user to update a database containing the information on malicious devices and data.

Date: 2024
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