Transparent Peer Review By Scholar9

DESIGN AND IMPLEMENTATION OF Wi-Fi DEAUTHENTICATION SYSTEM USING NODEMCU ESP8266

Abstract

Network security is seriously threatened by Wi-Fi de-authentication attacks, which frequently lead to data interception, illegal access, and service interruption. The mechanics and ramifications of these assaults are explored in detail in this research study, which highlights how they could jeopardize network availability, secrecy, and integrity. To bridge theoretical understanding with actual experimentation, the paper presents a practical implementation of a Wi-Fi deauther utilizing the NodeMCU ESP8266 microcontroller platform. With the use of programs like the Arduino IDE and NodeMCU Flasher, the Wi-Fi deauther was created and put through testing to identify and stop de-authentication threats instantly. The system's high detection accuracy, quick response times, and little effect on network performance as a whole are demonstrated by the experimental findings. The NodeMCU ESP8266 platform demonstrated good resource management by managing the detection and countermeasures while keeping CPU use below 70% and guaranteeing less than 5% reduction in network performance and latency. This study advances wireless network security by demonstrating a scalable, affordable method of thwarting de-authentication attacks and by suggesting further improvements that would include machine learning integration and wider assault coverage. For network managers, cybersecurity experts, and researchers looking to strengthen wireless network defenses, the findings offer insightful information and useful recommendations.

Imran Khan Reviewer

Review Request Accepted

Imran Khan Reviewer

Approved Rating

Relevance and Originality

Methodology

Validity & Reliability

Clarity and Structure

Results and Analysis

Comment

Relevance and Originality

The research article addresses a significant and timely concern in network security, specifically focusing on Wi-Fi de-authentication attacks. These attacks pose a considerable threat to the confidentiality, integrity, and availability of wireless networks, making the study highly relevant to current cybersecurity efforts. The originality of the research lies in the practical implementation of a Wi-Fi deauther using the NodeMCU ESP8266 platform, which is a scalable and cost-effective approach. The exploration of real-world applications and future integration with machine learning also adds value and innovation to the study.

Methodology

The methodology is well-detailed, as the research includes both theoretical explanations and practical experimentation. The use of the NodeMCU ESP8266 microcontroller platform, combined with tools like the Arduino IDE and NodeMCU Flasher, is an effective choice for building and testing the Wi-Fi deauther. The article clearly outlines the steps taken in creating and deploying the system, making the process reproducible. However, more information on the testing environment, such as network conditions or the number of test cases, would improve transparency. Additionally, discussing the limitations of the methodology could help readers understand the scope and potential challenges.

Validity & Reliability

The experimental results provided in the article demonstrate that the system has high detection accuracy and quick response times, with minimal impact on network performance. The reliability of the system is supported by quantitative data, such as CPU usage below 70% and a network performance reduction of less than 5%, which are solid indicators of the system's effectiveness. However, to strengthen the validity, more extensive testing in different real-world environments or on various types of networks would provide a broader evaluation of the system's performance.

Clarity and Structure

The article is well-organized and clearly explains the research problem, the methodology, and the outcomes of the experiment. The language is accessible, even for readers who may not be deeply familiar with the technical aspects of cybersecurity. The logical flow from the problem statement to the proposed solution and experimental results helps maintain clarity throughout. However, adding diagrams or flowcharts illustrating the attack mechanics and the architecture of the Wi-Fi deauther would enhance understanding, particularly for more visual learners.

Result Analysis

The article presents strong experimental results that demonstrate the system’s capability in detecting and mitigating Wi-Fi de-authentication attacks with minimal performance impact. The performance metrics, such as CPU usage and latency, are well-presented and relevant to evaluating the system’s efficiency. However, a more detailed comparative analysis with existing methods or commercial solutions could provide deeper insight into the system’s relative strengths and weaknesses. Additionally, the article could explore more on future improvements, such as the suggested integration with machine learning for better adaptability and wider coverage against various types of attacks.