M-IDM: A Multi-Classification Based Intrusion Detection Model in Healthcare IoT

Lee, Jae Dong; Soung, Hyo; Rathore, Shailendra; Park, Jong Hyuk

doi:10.32604/cmc.2021.014774

Cited by 12 publications

(2 citation statements)

References 33 publications

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“…A Multi-class classification Intrusion Detection Model (M-IDM) for healthcare IoT is proposed in [ 20 ] by employing a Convolutional Neural Network (CNN). It operates by seeking the byte sequence of the network traffic and known hostile malware patterns.…”

Section: Related Workmentioning

confidence: 99%

Security Analysis for Smart Healthcare Systems

Ibrahim,

Al-Wadi,

Elhafiz

2024

Sensors

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The healthcare industry went through reformation by integrating the Internet of Medical Things (IoMT) to enable data harnessing by transmission mediums from different devices, about patients to healthcare staff devices, for further analysis through cloud-based servers for proper diagnosis of patients, yielding efficient and accurate results. However, IoMT technology is accompanied by a set of drawbacks in terms of security risks and vulnerabilities, such as violating and exposing patients’ sensitive and confidential data. Further, the network traffic data is prone to interception attacks caused by a wireless type of communication and alteration of data, which could cause unwanted outcomes. The advocated scheme provides insight into a robust Intrusion Detection System (IDS) for IoMT networks. It leverages a honeypot to divert attackers away from critical systems, reducing the attack surface. Additionally, the IDS employs an ensemble method combining Logistic Regression and K-Nearest Neighbor algorithms. This approach harnesses the strengths of both algorithms to improve attack detection accuracy and robustness. This work analyzes the impact, performance, accuracy, and precision outcomes of the used model on two IoMT-related datasets which contain multiple attack types such as Man-In-The-Middle (MITM), Data Injection, and Distributed Denial of Services (DDOS). The yielded results showed that the proposed ensemble method was effective in detecting intrusion attempts and classifying them as attacks or normal network traffic, with a high accuracy of 92.5% for the first dataset and 99.54% for the second dataset and a precision of 96.74% for the first dataset and 99.228% for the second dataset.

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Section: Related Workmentioning

confidence: 99%

Security Analysis for Smart Healthcare Systems

Ibrahim,

Al-Wadi,

Elhafiz

2024

Sensors

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“…In Ref. [55], Lee et al proposed a Multiclass classification based Intrusion Detection Model (M-IDM) in e-health IoT. The proposed model exploits data from healthcare sensors, such as electrocardiograms and thermometers.…”

Section: Literature Surveymentioning

confidence: 99%

An Ensemble Learning Based Intrusion Detection Model for Industrial IoT Security

Mohy-eddine

Guezzaz

Benkirane

et al. 2023

Big Data Min. Anal.

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Industrial Internet of Things (IIoT) represents the expansion of the Internet of Things (IoT) in industrial sectors. It is designed to implicate embedded technologies in manufacturing fields to enhance their operations.However, IIoT involves some security vulnerabilities that are more damaging than those of IoT. Accordingly, Intrusion Detection Systems (IDSs) have been developed to forestall inevitable harmful intrusions. IDSs survey the environment to identify intrusions in real time. This study designs an intrusion detection model exploiting feature engineering and machine learning for IIoT security. We combine Isolation Forest (IF) with Pearson's Correlation Coefficient (PCC) to reduce computational cost and prediction time. IF is exploited to detect and remove outliers from datasets. We apply PCC to choose the most appropriate features. PCC and IF are applied exchangeably (PCCIF and IFPCC). The Random Forest (RF) classifier is implemented to enhance IDS performances. For evaluation, we use the Bot-IoT and NF-UNSW-NB15-v2 datasets. RF-PCCIF and RF-IFPCC show noteworthy results with 99.98% and 99.99% Accuracy (ACC) and 6.18 s and 6.25 s prediction time on Bot-IoT, respectively. The two models also score 99.30% and 99.18% ACC and 6.71 s and 6.87 s prediction time on NF-UNSW-NB15-v2, respectively. Results prove that our designed model has several advantages and higher performance than related models.

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