Machine Learning and Deep Learning Methods for Intrusion Detection Systems in IoMT: A survey

Rbah, Yahya; Mahfoudi, Mohammed; Balboul, Younes; Fattah, Mohammed Y.; Mazer, Saïd; Elbekkali, Moulhime; Bernoussi, Benaissa

doi:10.1109/iraset52964.2022.9738218

Cited by 42 publications

(24 citation statements)

References 59 publications

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“…To determine the DEML model's overall efciency, various evaluation metrics are applied, as given in equations (32)(33)(34)(35)(36)(37). Te accuracy measure can be used to show the overall predicting capabilities of the ML models and the proposed fused weighted DEML model [51]. True positive (TP) and true negative (TN) determine the competence of the suggested classifer to predict the absence and presence of lung cancer disease in the confusion matrix.…”

Section: Performance Evaluationmentioning

confidence: 99%

Fused Weighted Federated Deep Extreme Machine Learning Based on Intelligent Lung Cancer Disease Prediction Model for Healthcare 5.0

Abbas

Issa

Fatima

et al. 2023

International Journal of Intelligent Systems

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In the era of advancement in information technology and the smart healthcare industry 5.0, the diagnosis of human diseases is still a challenging task. The accurate prediction of human diseases, especially deadly cancer diseases in the smart healthcare industry 5.0, is of utmost importance for human wellbeing. In recent years, the global Internet of Medical Things (IoMT) industry has evolved at a dizzying pace, from a small wristwatch to a big aircraft. With this advancement in the healthcare industry, there also rises the issue of data privacy. To ensure the privacy of patients’ data and fast data transmission, federated deep extreme learning entangled with the edge computing approach is considered in this proposed intelligent system for the diagnosis of lung disease. Federated deep extreme machine learning is applied for the prediction of lung disease in the proposed intelligent system. Furthermore, to strengthen the proposed model, a fused weighted deep extreme machine learning methodology is adopted for better prediction of lung disease. The MATLAB 2020a tool is used for simulation and results. The proposed fused weighted federated deep extreme machine learning model is used for the validation of the best prediction of cancer disease in the smart healthcare industry 5.0. The result of the proposed fused weighted federated deep extreme machine learning approach achieved 97.2%, which is better than the state-of-the-art published methods.

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Section: Performance Evaluationmentioning

confidence: 99%

Fused Weighted Federated Deep Extreme Machine Learning Based on Intelligent Lung Cancer Disease Prediction Model for Healthcare 5.0

Abbas

Issa

Fatima

et al. 2023

International Journal of Intelligent Systems

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“…Hameed for IDSs in the IoMT. They observe that many researchers develop their approaches in an isolated environment for a limited number of attacks [16]. Pelekoudas-Oikonomou et al review blockchain-based security mechanisms for IoMT edge networks.…”

Section: Related Workmentioning

confidence: 99%

Attack Detection for Medical Cyber-Physical Systems–A Systematic Literature Review

et al. 2023

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The threat situation due to cyber attacks in hospitals is emerging and patient life is at risk. One significant source of potential vulnerabilities is medical cyber-physical systems (MCPS). Detecting intrusions in this environment faces challenges different from other domains, mainly due to the heterogeneity of devices, the diversity of connectivity types, and the variety of terminology. To summarize existing results, we conducted a structured literature review (SLR) following the guidelines of Kitchenham et al. for SLRs in software engineering. We developed six research questions regarding detection approach, detection location, included features, adversarial focus, utilized datasets, and intrusion prevention. We identified that most researchers focused on an anomaly-based detection approach at the network layer. The primary focus was on the detection of malicious insiders. While several researchers used publicly available datasets for training and testing their algorithms, the lack of suitable datasets resulted in the development of testbeds consisting of various medical devices. Based on the results, we formulated five future research topics. First, the special conditions of hospital networks, the MCPS deployed within them, and the contrasts to other IT and OT environments should be examined. Thereupon, MCPS-specific datasets should be created that allow researchers to address the health domain's unique requirements and possibilities. At the same time, endeavors aimed at standardization in this area should be supported and expanded. Moreover, the use of medical context for attack detection should be further explored. Last but not least, efforts for MCPS-tailored intrusion prevention should be intensified. This way, the emerging threat landscape can be addressed, IT security in hospitals can be improved, and patient health can be protected.

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“…There are many researchers who have provided good overviews in the arena of IoMT. Shamsoshoara et al [ 18 ] provided security measures using PUF; Fernández-Caramés et al [ 19 ] showed the challenges of security measures; Alwarafy et al [ 20 ] presented intrusion detection using edge computing; Shakeel et al [ 21 ] showed security systems in limited scale; Al-Garadi et al [ 22 ], Arora et al [ 23 ], and Rbah et al [ 24 ] presented deep-learning based intrusion detection system; and only blockchain-based measures are presented by Sengupta et al [ 17 ], Sawadi et al [ 25 ], and Khor et al [ 26 ]. This paper presents not only the issues and challenges of IoMT application that are highlighted, but security and privacy risks with both centralized and decentralized solutions are presented.…”

Section: Contributionsmentioning

confidence: 99%

Prospect of Internet of Medical Things: A Review on Security Requirements and Solutions

Sadhu

Yanambaka

Abdelgawad

et al. 2022

Sensors

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With the widespread and increasing use of Internet-of-Things (IoT) devices in all aspects of daily life, a hopeful future for people, data, and processes is emerging. Extensive spans allow for an integrated life cycle to be created from home to enterprise. The Internet of Medical things (IoMT) forms a flourishing surface that incorporates the sensitive information of human life being sent to doctors or hospitals. These open an enormous space for hackers to utilize flaws of the IoMT network to make a profit. This creates a demand for standardizing regulations and a secure system. Though many authorities are making standards, there are some lacking in the system which makes the product vulnerable. Although many established mechanisms are present for the IoT network, there are a number of obstacles preventing its general implementation in the IoMT network. One of the adoption challenges is the IoMT devices itself, because many IoMT networks consist of battery-powered devices with constrained processing capability. A general overview of the different security integrations with IoT applications has been presented in several papers. Therefore, this paper aims to provide an overview of the IoMT ecosystem, regulations, challenges of standards, security mechanisms using cryptographic solutions, physical unclonable functions (PUF)-based solutions, blockchain, and named data networking (NDN) as well, with pros and cons.

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Machine Learning and Deep Learning Methods for Intrusion Detection Systems in IoMT: A survey

Cited by 42 publications

References 59 publications

Fused Weighted Federated Deep Extreme Machine Learning Based on Intelligent Lung Cancer Disease Prediction Model for Healthcare 5.0

Fused Weighted Federated Deep Extreme Machine Learning Based on Intelligent Lung Cancer Disease Prediction Model for Healthcare 5.0

Attack Detection for Medical Cyber-Physical Systems–A Systematic Literature Review

Prospect of Internet of Medical Things: A Review on Security Requirements and Solutions

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