Comparative Analysis of Machine Learning Techniques for Intrusion Detection In Nigerian IoT Networks: A Systematic Literature Review

Abstract

A large part of the increase in cyber threats to the network of Nigeria is the introduction of IoT devices in the areas of healthcare, smart homes, transportation, surveillance, banking, and business. These cyber threats include the likes of DDoS, malware injection, spoofing, unauthorized access. In this paper, we summarize a literature review and a comparative analysis of the techniques used in machine learning for intrusion detection in IoT networks, with a focus on their suitability for resource-constrained environments in Nigeria. PRISMA framework and Parsifal guided the process of reviewing and managing. Out of 850 records discovered on the different online platforms that are IEEE Xplore, Scopus, ScienceDirect, SpringerLink, and Google Scholar, the research documented 30 peer-reviewed studies which were published between 2021 and 2026 and included 30 papers for final synthesis. The most commonly used techniques in the IoT intrusion detection literature are Random Forest, Support Vector Machine, Decision Tree, K-Nearest Neighbors, Artificial Neural Networks, Convolutional Neural Networks (CNN), and Long ShortTerm Memory (LSTM) models. CNN and LSTM models usually had the highest reported detection scores, being about 96-99% in accuracy, precision, recall, and F1-score; however, their high computational and memory requirements limit their practical deployment on low-power IoT devices. Random Forest is evaluated as the best choice because of its appropriate overall balance in terms of reporting high accuracy statistics (95-99%) and also the moderate computational cost with the strong suitability rating for Nigeria's current IoT infrastructure. Decision Tree and KNN were also mentioned as lightweight alternatives, but they could stand lower performance compared to the complex attacks. The study declares that for Nigeria's IoT cybersecurity resilience to be effective, it is imperative to have accurate, lightweight, explainable, and context-aware intrusion detection model.