1. Project Title

Intrusion Detection Model for Internet of Things to Enhance Security of Access Points on Wireless Local Area Networks

2. Project Description

Objectives:

To develop an intrusion detection model specifically designed for IoT devices and wireless access points, utilizing sniffing tools and monitoring techniques to identify and alert on security threats in wireless network environments.

Scope:

This research addresses the growing security challenges posed by the proliferation of IoT devices on wireless networks. It focuses on developing detection mechanisms for identifying unauthorized access, suspicious activities, and potential security breaches on wireless sensor networks and IoT ecosystems.

Methodology:

Security analysis of IoT devices and wireless access points, implementation and evaluation of sniffing tools for network traffic monitoring, development of intrusion detection algorithms and rule-based systems, machine learning approaches for anomaly detection, testing and validation in simulated IoT network environments, and performance evaluation of the detection model.

Key Outputs:

Completed MSc thesis by Joseph Gitonga Imathiu (2025) under Dr. Kirongo’s supervision, published research article ‘Security intrusion monitoring model for Internet of Things (IoT) using sniffing tools on wireless sensor networks’ in the African Journal of Science, Technology and Social Sciences (2023), and functional intrusion detection prototype for IoT networks.

Partnerships/Funding:

Meru University of Science and Technology

Duration:

Research period: 2021-2025 (MSc thesis supervised)

Anticipated Impact:

The intrusion detection model enhances security of IoT deployments, which is critical as IoT devices become increasingly prevalent in homes, businesses, and critical infrastructure. The research contributes to making IoT ecosystems more secure and trustworthy, addressing one of the major barriers to IoT adoption. The model can be deployed to protect wireless networks in educational institutions, healthcare facilities, and other organizations implementing IoT solutions.

3. Persons Responsible

Joseph Gitonga Imathiu (MSc Student – CT402/202192/21), Dr. Amos Chege Kirongo (Thesis Supervisor), Collaborators: A. Chege, A. Omamo

4. Current Status

Completed – MSc thesis successfully defended (2025). Publication completed.