Evaluation of Various Machine Learning Algorithms for Detection of Attacks in 5G

Stephen, Colin; Mathesh, R.; Venkat, L.; Abishek, B. Ebenezer; Vijayalakshmi, A.

doi:10.1007/978-981-16-4943-1_37

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2023

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“…Using the information that was received from the logs, Arul Stephen et. al [6] analysed the functionality of the IoT Network. Several methods for supervised machine learning have been utilised throughout the course of the inquiry.…”

Section: Related Workmentioning

confidence: 99%

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Detection of various attacks using B-GNB in health care IoT

Arul¹,

Vijayalakshmi²,

Abishek³

2023

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Despite various advances in health care applications, Intrusion detection remains a challenging task in wireless network due to numerous amount of traffic and large amount of sensitive data leading to various cyber attacks (DDoS, MITM, scan etc).The sensors are used to collect the real time data of patients or wearable devices and send the same to doctors for analyzing various diseases in early stages using IoT. Wireless network is vulnerable to intruders ,mainly for carrying sensitive information hence this paper proposes supervised machine learning technique B-GNB to detect various attacks in Health care data in IuT.The proposed model chooses the best features and accurately classifies attacks with 96% accuracy.

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Section: Related Workmentioning

confidence: 99%

“…The Open-Source IoT Device Network Logs dataset was collected from the Kaggle [6]. In this Dataset there are 477426 data, 13 input features, and one output label class called normality.…”

Section: Dataset Collection and Descriptionmentioning

confidence: 99%