Wi-Fi Data Analysis Based on Machine Learning - Wi-Fi network security intrusion detection system (WNIDS)

Aug 2, 2026

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This study proposes using machine learning to strengthen Wi‑Fi network security amid expanding connectivity from industrial to residential settings. It addresses smart networking and IoT-driven data vulnerability by collecting, preprocessing, and analyzing network traffic to build a comprehensive dataset. The dataset trains machine learning models that classify traffic as normal or specific attacks through a two-stage WNIDS design. The deployed system targets diverse threats and reduces network weaknesses across domains, enabling safe, reliable access for users globally.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Introduction

Deep Learning-based Intrusion Detection for Wi-Fi Networks

Machine Learning Techniques for Wi-Fi Intrusion Detection

WI-FI DATA ANALYSIS BASED ON MACHINE LEARNING Nanumura Gedara Umal Anuraga Nanumura University of South Wales, United Kingdom ABSTRACT This study proposes using machine learning to improve Wi-Fi network security. As Wi-Fi networks spread from industrial to residential areas, the necessity for strong security has risen. The rise of smart networking, especially in the IoT, has created data security and vulnerability issues. A unique method that uses machine learning to detect abnormalities and probable security breaches in Wi-Fi networks addresses these difficulties. We gather, preprocess, and analyse network data to create a complete dataset. This dataset trains machine learning algorithms to identify and classify network anomalies. Using agile methods, data mining, and machine learning algorithms, we created a Wi-Fi network intrusion detection system (WNIDS) that can detect diverse network threats. The proposed WNIDS contains two linked stages with specific machine learning models. These algorithms accurately classify network data as normal or attack specific. Our technology protects against malicious attacks and provides a robust Wi-Fi network for users across domains by incorporating machine learning. Modern network security dangers were fully understood by surveys and data analysis. The WNIDS was implemented and deployed through a structured system development life cycle. This tool eliminates network weaknesses and advances distant enterprises, offering safe and smooth access for consumers globally. KEYWORDS Machine learning, Wi-Fi network security, data security, vulnerability, dataset. 1. INTRODUCTION The way in which people engage with technology has been fundamentally altered as a result of the widespread availability of mobile computing devices, in particular smartphones, and the rising processing capacity of these devices [1] . These gadgets have become indispensable to dayto-day life since they make possible a diverse array of activities such as communication, the exchange of documents, video streaming, and many more. The proliferation of cloud-based services has added to the already enormous volume of Internet traffic, which has led to projections of a significant rise in the amount of data that is transmitted over the Internet [2] . WiFi networks, which account for a sizeable percentage of this expansion, have developed to meet the need for connectivity posed by portable devices [3] . However, the proliferation of Wi-Fi networks has also resulted in an increase in worries over their level of security. Because of the broad use of these networks, they have grown susceptible to unwanted access and infiltration as a result. Instances of network breaches, illegal control of public Wi-Fi networks, and flaws in encryption techniques such as WPA2 have brought to light the essential requirement for highly effective security procedures [4] . It is of the utmost importance, not only for individual users but also for sectors that are dependent on network connectivity, to protect the privacy, authenticity, and accessibility of the data that is transmitted through Wi-Fi networks [5] . The deployment of methods that utilize machine learning has become more popular as a means of addressing these security concerns. To identify unusual occurrences and potential security risks, machine learning analyzes massive volumes of data for recurring patterns. These methods, when applied to Wi-Fi networks, have the ability to recognize odd network behavior, differentiate between genuine and malicious access points, and increase overall network security [6] . The purpose of this study is to investigate how the power of machine learning may be utilized to strengthen the security of Wi-Fi networks. This study makes a contribution to the protection of both human and corporate data by detecting illegal access, identifying hostile network activity, and proactively addressing possible threats. These are all ways in which the d

FAQs

What problem does the study address in Wi‑Fi networks?

The study targets increasing Wi‑Fi security concerns, including unauthorized access, infiltration, and weaknesses in encryption such as WPA2.

How does the proposed system detect intrusions?

It gathers and preprocesses Wi‑Fi network data, trains machine learning algorithms, and uses a two-stage WNIDS approach to classify traffic as normal or attacks.

Which machine learning approaches are mentioned for intrusion detection?

The text highlights deep learning with CNNs and evaluates machine learning models such as Random Forest, Support Vector Machines (SVM), and Neural Networks.

Aug 2, 2026

Upload by Anda