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Wi-Fi Data Analysis Based On Machine Learning


Affiliations
1 University of South Wales, United Kingdom
 

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.
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  • S. F. Ng, N. S. I. Che-Hassan, N. H. Mohammad-Nor, and N. A. Abdul-Malek, “The Relationship between Smartphone Use, Symptoms of Depression, Symptoms of Anxiety, and Academic Performance in College Students,” Malaysian Online J. Educ. Technol., vol. 5, no. 4, p. 72, 2017, [Online]. Available: http://www.mojet.net/frontend//articles/pdf/v5i4/v05i04-05pdf.pdf.

  • S. Kumar, P. Tiwari, and M. Zymbler, “Internet of Things is a revolutionary approach for future technology enhancement: a review,” J. Big Data, vol. 6, no. 1, pp. 1–21, Dec. 2019, doi: 10.1186/S40537-019-0268-2/FIGURES/9.

  • T. Cisco and A. Internet, “Cisco: 2020 CISO Benchmark Report,” Comput. Fraud Secur., vol. 2020, no. 3, pp. 4–4, 2020, doi: 10.1016/s1361-3723(20)30026-9.

  • J. Jang-Jaccard and S. Nepal, “A survey of emerging threats in cybersecurity,” J. Comput. Syst. Sci., vol. 80, no. 5, pp. 973–993, Aug. 2014, doi: 10.1016/J.JCSS.2014.02.005.

  • B. L. Filkins et al., “Privacy and security in the era of digital health: What should translational researchers know and do about it?,” Am. J. Transl. Res., vol. 8, no. 3, pp. 1560–1580, 2016.

  • I. H. Sarker, “Machine Learning for Intelligent Data Analysis and Automation in Cybersecurity: Current and Future Prospects,” Ann. Data Sci., pp. 1–26, Sep. 2022, doi: 10.1007/S40745-022-00444-2/FIGURES/6.

  • A. F. J. Jasim and S. Kurnaz, “New automatic (IDS) in IoTs with artificial intelligence technique,” Optik (Stuttg)., vol. 273, p. 170417, 2023, doi: https://doi.org/10.1016/j.ijleo.2022.170417.

  • R. Nazir, A. A. laghari, K. Kumar, S. David, and M. Ali, “Survey on Wireless Network Security,” Arch. Comput. Methods Eng., vol. 29, no. 3, pp. 1591–1610, May 2022, doi: 10.1007/S11831-021-09631-5.

  • M. Natkaniec and M. Bednarz, “Wireless Local Area Networks Threat Detection Using 1D-CNN,” Sensors, vol. 23, no. 12, p. 5507, 2023, doi: 10.3390/s23125507.

  • T. Sowmya and E. A. Mary Anita, “A comprehensive review of AI based intrusion detection system,” Meas. Sensors, vol. 28, p. 100827, Aug. 2023, doi: 10.1016/J.MEASEN.2023.100827.

  • M. M. Rashid, J. Kamruzzaman, M. M. Hassan, T. Imam, and S. Gordon, “Cyberattacks detection in iot-based smart city applications using machine learning techniques,” Int. J. Environ. Res. Public Health, vol. 17, no. 24, pp. 1–21, 2020, doi: 10.3390/ijerph17249347.

  • Y. Kayode Saheed, A. Idris Abiodun, S. Misra, M. Kristiansen Holone, and R. Colomo-Palacios, “A machine learning-based intrusion detection for detecting internet of things network attacks,” Alexandria Eng. J., vol. 61, no. 12, pp. 9395–9409, Dec. 2022, doi: 10.1016/J.AEJ.2022.02.063.

  • P. Vanin et al., “A Study of Network Intrusion Detection Systems Using Artificial Intelligence/Machine Learning,” Appl. Sci., vol. 12, no. 22, 2022, doi: 10.3390/app122211752.

  • Y. Song, S. Hyun, and Y. G. Cheong, “Analysis of autoencoders for network intrusion detection†,” Sensors, vol. 21, no. 13, pp. 1–23, 2021, doi: 10.3390/s21134294.

  • T. Sowmya and E. A. Mary Anita, “A comprehensive review of AI based intrusion detection system,” Meas. Sensors, vol. 28, p. 100827, 2023, doi: https://doi.org/10.1016/j.measen.2023.100827.

  • H. Premakumar, A. Kunarathinam, and V. Ravi, “Detection of Intrusions in networks using Machine Learning Approach,” pp. 1–7, 2022.

  • E. Ul, H. Qazi, M. H. Faheem, and T. Zia, “HDLNIDS: Hybrid Deep-Learning-Based Network Intrusion Detection System,” Appl. Sci. 2023, Vol. 13, Page 4921, vol. 13, no. 8, p. 4921, Apr. 2023, doi: 10.3390/APP13084921.

  • J. J. Montaño Moreno, A. Palmer Pol, and P. Muñoz Gracia, “Artificial neural networks applied to forecasting time series.,” Psicothema, vol. 23, no. 2, pp. 322–329, Apr. 2011.

  • İ. Yazici, I. Shayea, and J. Din, “A survey of applications of artificial intelligence and machine learning in future mobile networks-enabled systems,” Eng. Sci. Technol. an Int. J., vol. 44, p. 101455, 2023, doi: https://doi.org/10.1016/j.jestch.2023.101455.

  • R. Kaur, D. Gabrijelčič, and T. Klobučar, “Artificial intelligence for cybersecurity: Literature review and future research directions,” Inf. Fusion, vol. 97, p. 101804, 2023, doi: https://doi.org/10.1016/j.inffus.2023.101804.

  • V. Rey, P. M. Sánchez Sánchez, A. Huertas Celdrán, and G. Bovet, “Federated learning for malware detection in IoT devices,” Comput. Networks, vol. 204, p. 108693, 2022, doi: https://doi.org/10.1016/j.comnet.2021.108693.

  • K. Bicakci and B. Tavli, “Denial-of-Service attacks and countermeasures in IEEE 802.11 wireless networks,” Comput. Stand. Interfaces, vol. 31, no. 5, pp. 931–941, Sep. 2009, doi: 10.1016/j.csi.2008.09.038.

  • M. Wang, G. Song, Y. Yu, and B. Zhang, “The Current Research Status of AI-Based Network Security Situational Awareness,” Electron., vol. 12, no. 10, 2023, doi: 10.3390/electronics12102309.

  • Huawei, “display mac-address - Fat AP and Cloud AP V200R010C00 Command Reference - Huawei,” 2023. https://support.huawei.com/enterprise/en/doc/EDOC1100064352/25e69c90/display-mac-address (accessed Aug. 13, 2023).

  • A. T. Chala and R. Ray, “Assessing the Performance of Machine Learning Algorithms for Soil Classification Using Cone Penetration Test Data,” Appl. Sci., vol. 13, no. 9, 2023, doi: 10.3390/app13095758.


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  • Wi-Fi Data Analysis Based On Machine Learning

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Authors

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.

References