Cyberattacks Detection in IoMT using Machine Learning Techniques

Tauqeer, Haseeb; Iqbal, Muhammad Munwar; Ali, Aatka; Zaman, Shakir; Chaudhry, Muhammad Umar

doi:10.56979/401/2022/80

Cited by 11 publications

(8 citation statements)

References 16 publications

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“…In the framework of the IoMT, the paper [14] tackles the crucial cybersecurity problem. The authors aimed to create efficient techniques for the identification and cessation of cyberattacks in IoMT environments.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Navigating the Cyber Threat Landscape: An In-Depth Analysis of Attack Detection within IoT Ecosystems

AboulEla,

Ibrahim,

Shehmir

et al. 2024

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The Internet of Things (IoT) is seeing significant growth, as the quantity of interconnected devices in communication networks is on the rise. The increased connectivity of devices has heightened their susceptibility to hackers, underscoring the need to safeguard IoT devices. This research investigates cybersecurity in the context of the Internet of Medical Things (IoMT), which encompasses the cybersecurity mechanisms used for various healthcare devices connected to the system. This study seeks to provide a concise overview of several artificial intelligence (AI)-based methodologies and techniques, as well as examining the associated solution approaches used in cybersecurity for healthcare systems. The analyzed methodologies are further categorized into four groups: machine learning (ML) techniques, deep learning (DL) techniques, a combination of ML and DL techniques, Transformer-based techniques, and other state-of-the-art techniques, including graph-based methods and blockchain methods. In addition, this article presents a detailed description of the benchmark datasets that are recommended for use in intrusion detection systems (IDS) for both IoT and IoMT networks. Moreover, a detailed description of the primary evaluation metrics used in the analysis of the discussed models is provided. Ultimately, this study thoroughly examines and analyzes the features and practicality of several cybersecurity models, while also emphasizing recent research directions.

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Section: Machine Learning Methodsmentioning

confidence: 99%

Navigating the Cyber Threat Landscape: An In-Depth Analysis of Attack Detection within IoT Ecosystems

AboulEla,

Ibrahim,

Shehmir

et al. 2024

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“…When evaluating a network's performance, the routing protocol should be taken into account, as suggested by Jayalakshmi et al [20], who argued in favor of strengthening network security by using cryptography at every node. As proposed by Almomani [26], based on these suggestions, NIDSs can incorporate feature selection models to enhance their functionality. The development of this concept was motivated by the multitude of optimization strategies available, such as the particle swarm optimization (PSO), grey wolf optimization (GWO), firefly algorithm (FFA), and genetic algorithm (GA).…”

Section: Related Workmentioning

confidence: 99%

“…In order to spot cyberattacks, Tauqeer et al [26] employs a trifecta of Machine Learning algorithms: Random Forest, Gradient Boosting, and Support Vector Machine (SVM). The most effective models for detecting cyberattack are those trained with machine learning.…”

Section: Related Workmentioning

confidence: 99%

Stochastic Gradient Descent Intrusions Detection for Wireless Sensor Network Attack Detection System Using Machine Learning

Saleh,

Marouane,

Fakhfakh

2024

IEEE Access

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Communication in cyber-physical systems relies heavily on Wireless Sensor Networks (WSNs), which have numerous uses including ambient monitoring, object recognition, and data transmission. However, they are vulnerable to cyberattacks because they are connected to the IoT. In order to combat the difficulties associated with WSN intrusion detection, this research employs machine learning techniques, notably the Gaussian Nave Bayes (GNB) and Stochastic Gradient Descent (SGD) algorithms. The effectiveness of recommendation systems is improved with the introduction of context awareness. To lessen the burden on the computer, we first do a principal component analysis and singular value decomposition on the raw traffic data. The system was tested on two datasets, yielding extremely high accuracy results. This is evidence of the system's strength, even when the dataset is changed. On the WSN-DS dataset, the suggested SG-IDS model achieved a 96% accuracy rate, outperforming state-of-the-art algorithms with higher rates of 98% accuracy, 96% recall, and 97% F1-measurement. In an evaluation on an IoMT dataset, the SG-IDS performed admirably, with an accuracy of 0.87 and a precision of 1.00 in intrusion detection tasks.

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“…There have been multiple cyberattacks on the healthcare business, and millions of people's data have been released online. Upon analysis of different research [14,15] regarding cybersecurity in the healthcare sector, it is observed that those approaches only utilize a single technology. This results in a dilemma of whether to store the patient data securely or to analyze the data for tampering with the patient's health data.…”

Section: Motivationmentioning

confidence: 99%

A Trustworthy Healthcare Management Framework Using Amalgamation of AI and Blockchain Network

Jadav

Gupta

et al. 2023

Mathematics

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Over the last few decades, the healthcare industry has continuously grown, with hundreds of thousands of patients obtaining treatment remotely using smart devices. Data security becomes a prime concern with such a massive increase in the number of patients. Numerous attacks on healthcare data have recently been identified that can put the patient’s identity at stake. For example, the private data of millions of patients have been published online, posing a severe risk to patients’ data privacy. However, with the advent of Industry 4.0, medical practitioners can digitally assess the patient’s condition and administer prompt prescriptions. However, wearable devices are also vulnerable to numerous security threats, such as session hijacking, data manipulation, and spoofing attacks. Attackers can tamper with the patient’s wearable device and relays the tampered data to the concerned doctor. This can put the patient’s life at high risk. Since blockchain is a transparent and immutable decentralized system, it can be utilized for securely storing patient’s wearable data. Artificial Intelligence (AI), on the other hand, utilizes different machine learning techniques to classify malicious data from an oncoming stream of patient’s wearable data. An amalgamation of these two technologies would make the possibility of tampering the patient’s data extremely difficult. To mitigate the aforementioned issues, this paper proposes a blockchain and AI-envisioned secure and trusted framework (HEART). Here, Long-Short Term Model (LSTM) is used to classify wearable devices as malicious or non-malicious. Then, we design a smart contract that allows only of those patients’ data having a wearable device to be classified as non-malicious to the public blockchain network. This information is then accessible to all involved in the patient’s care. We then evaluate the HEART’s performance considering various evaluation metrics such as accuracy, recall, precision, scalability, and network latency. On the training and testing sets, the model achieves accuracies of 93% and 92.92%, respectively.

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Cyberattacks Detection in IoMT using Machine Learning Techniques

Cited by 11 publications

References 16 publications

Navigating the Cyber Threat Landscape: An In-Depth Analysis of Attack Detection within IoT Ecosystems

Navigating the Cyber Threat Landscape: An In-Depth Analysis of Attack Detection within IoT Ecosystems

Stochastic Gradient Descent Intrusions Detection for Wireless Sensor Network Attack Detection System Using Machine Learning

A Trustworthy Healthcare Management Framework Using Amalgamation of AI and Blockchain Network

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