A Fuzzy-Based Duo-Secure Multi-Modal Framework for IoMT Anomaly Detection

Wagan, Shiraz Ali; Koo, Jahwan; Siddiqui, Isma Farah; Qureshi, Nawab Muhammad Faseeh; Attique, Muhammad; Shin, Dong Ryeol

doi:10.1016/j.jksuci.2022.11.007

Cited by 26 publications

(12 citation statements)

References 43 publications

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“…The orthogonal tables utilized a smaller amount of trials to discover the optimal combination for every factor with various levels. The ) value is derived as shown in equation (22), and the best combination is determined using just 8 tests.…”

Section: Orthogonal Opposition-based Learning (Oobl) Approachmentioning

confidence: 99%

“…Wagan et al 22 proposed the duo‐secure IoMT framework using multimodal data signal that shows the variation of different attack patterns and data from conventional healthcare devices, while Nandy et al 23 proposed an empirical intelligent agent (EIA) based on a Swarm‐neural network (Swarm‐NN) algorithm to prevent privacy leakage in IoMT networks. Fang et al 24 developed an anomaly detection system for detecting illegal behavior (DIB) in a medical IoT environment using rough set theory and fuzzy core vector machine by rough set theory with fuzzy core vector machine (R‐FCVM).…”

Section: Review Of Related Workmentioning

confidence: 99%

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An optimal attention PLSTM‐based classification model to enhance the performance of IoMT attack detection in healthcare application

Dhanushkodi,

Shunmugiah,

Velladurai

et al. 2024

Trans Emerging Tel Tech

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The Internet of Medical Things (IoMT) has revolutionized the healthcare industry by allowing remote monitoring of patients suffering from chronic diseases. However, security concerns arise due to the potential life‐threatening damage that can be caused by attacks on IoMT devices. To enhance the security of IoMT devices, researchers propose the use of novel artificial intelligence‐based intrusion detection techniques. This article presents a hybrid alex net model and an orthogonal opposition‐based learning Yin‐Yang‐pair optimization (OOYO) optimized attention‐based Peephole long short term memory (PLSTM) model to distinguish between malicious and normal network traffic in the IoMT environment. To improve the scalability of the model in handling the random and dynamic behavior of malicious attacks, the hyper parameters of the PLSTM framework are optimized using the OOYO algorithm. The proposed model is evaluated on different IoT benchmark datasets such as N‐BaIoT and IoT healthcare security. Experimental results demonstrate that the proposed model provides a classification accuracy of 99% and 98% on the healthcare security and N‐BaIoT datasets, respectively. Moreover, the proposed model exhibits high generalization ability for multi‐class classifications and is effective in reducing the false discovery rate. Overall, the proposed model achieves high accuracy, scalability, and generalization ability in identifying malicious traffic, which can help improve the security solution of IoMT devices.

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Section: Orthogonal Opposition-based Learning (Oobl) Approachmentioning

confidence: 99%

Section: Review Of Related Workmentioning

confidence: 99%

An optimal attention PLSTM‐based classification model to enhance the performance of IoMT attack detection in healthcare application

Dhanushkodi,

Shunmugiah,

Velladurai

et al. 2024

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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“…Feature selection techniques play an important role in improving the performance of intrusion detection systems (IDS) in the Internet of Medical Things (IoMT) [ 25 , 35 ]. There are various feature selection algorithms that have been used in the literature to reduce the dimensionality of the input features while preserving their relevant information [ 36 ]. Filter-based methods, such as chi-square and information gain, evaluate each feature individually to determine its contribution to the target variable [ 30 ].…”

Section: Related Workmentioning

confidence: 99%

“…The use of deep learning algorithms for anomaly-based intrusion detection systems (IDS) in the Internet of Medical Things (IoMT) has gained significant attention in recent years [ 35 ]. Anomaly-based IDS aim to detect security incidents by identifying deviations from normal behavior in the network or devices [ 36 ]. Deep learning algorithms can be used to model this normal behavior and detect anomalies in real time.…”

Section: Introductionmentioning

confidence: 99%

An Improved Mutual Information Feature Selection Technique for Intrusion Detection Systems in the Internet of Medical Things

Alalhareth,

Hong

2023

Sensors

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In healthcare, the Internet of Things (IoT) is used to remotely monitor patients and provide real-time diagnoses, which is referred to as the Internet of Medical Things (IoMT). This integration poses a risk from cybersecurity threats that can harm patient data and well-being. Hackers can manipulate biometric data from biosensors or disrupt the IoMT system, which is a major concern. To address this issue, intrusion detection systems (IDS) have been proposed, particularly using deep learning algorithms. However, developing IDS for IoMT is challenging due to high data dimensionality leading to model overfitting and degraded detection accuracy. Feature selection has been proposed to prevent overfitting, but the existing methods assume that feature redundancy increases linearly with the size of the selected features. Such an assumption does not hold, as the amount of information a feature carries about the attack pattern varies from feature to feature, especially when dealing with early patterns, due to data sparsity that makes it difficult to perceive the common characteristics of selected features. This negatively affects the ability of the mutual information feature selection (MIFS) goal function to estimate the redundancy coefficient accurately. To overcome this issue, this paper proposes an enhanced feature selection technique called Logistic Redundancy Coefficient Gradual Upweighting MIFS (LRGU-MIFS) that evaluates candidate features individually instead of comparing them with common characteristics of the already-selected features. Unlike the existing feature selection techniques, LRGU calculates the redundancy score of a feature using the logistic function. It increases the redundancy value based on the logistic curve, which reflects the nonlinearity of the relationship of the mutual information between features in the selected set. Then, the LRGU was incorporated into the goal function of MIFS as a redundancy coefficient. The experimental evaluation shows that the proposed LRGU was able to identify a compact set of significant features that outperformed those selected by the existing techniques. The proposed technique overcomes the challenge of perceiving common characteristics in cases of insufficient attack patterns and outperforms existing techniques in identifying significant features.

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“…However, with the increasing connectivity of medical devices and systems, concerns have also grown in areas where it is difficult to have proper infrastructure. This is where unmanned aerial vehicles (UAVs) use in the context of secure communication systems in IoMT emerges as a promising solution [3]. UAVs, commonly known as drones, have proven helpful in various fields, and their potential applications in the healthcare industry are vast owing to their compatibility [4].…”

Section: Introductionmentioning

confidence: 99%

A UAV-Assisted Stackelberg Game Model for Securing loMT Healthcare Networks

Shaikh

Wang

Khan

et al. 2023

Drones

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On the one hand, the Internet of Medical Things (IoMT) in healthcare systems has emerged as a promising technology to monitor patients’ health and provide reliable medical services, especially in remote and underserved areas. On the other hand, in disaster scenarios, the loss of communication infrastructure can make it challenging to establish reliable communication and to provide timely first aid services. To address this challenge, unmanned aerial vehicles (UAVs) have been adopted to assist hospital centers in delivering medical care to hard-to-reach areas. Despite the potential of UAVs to improve medical services in emergency scenarios, their limited resources make their security critical. Therefore, developing secure and efficient communication protocols for IoMT networks using UAVs is a vital research area that can help ensure reliable and timely medical services. In this paper, we introduce a novel Stackelberg security-based game theory algorithm, named Stackelberg ad hoc on-demand distance vector (SBAODV), to detect and recover data affected by black hole attacks in IoMT networks using UAVs. Our proposed scheme utilizes the substantial Stackelberg equilibrium (SSE) to formulate strategies that protect the system against attacks. We evaluate the performance of our proposed SBAODV scheme and compare it with existing routing schemes. Our results demonstrate that our proposed scheme outperforms existing schemes regarding packet delivery ratio (PDR), networking load, throughput, detection ratio, and end-to-end delay. Specifically, our proposed SBAODV protocol achieves a PDR of 97%, throughput ranging from 77.7 kbps to 87.3 kbps, and up to 95% malicious detection rate at the highest number of nodes. Furthermore, our proposed SBADOV scheme offers significantly lower networking load (7% to 30%) and end-to-end delay (up to 30%) compared to existing routing schemes. These results demonstrate the efficiency and effectiveness of our proposed scheme in ensuring reliable and secure communication in IoMT emergency scenarios using UAVs.

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A Fuzzy-Based Duo-Secure Multi-Modal Framework for IoMT Anomaly Detection

Cited by 26 publications

References 43 publications

An optimal attention PLSTM‐based classification model to enhance the performance of IoMT attack detection in healthcare application

An optimal attention PLSTM‐based classification model to enhance the performance of IoMT attack detection in healthcare application

An Improved Mutual Information Feature Selection Technique for Intrusion Detection Systems in the Internet of Medical Things

A UAV-Assisted Stackelberg Game Model for Securing loMT Healthcare Networks

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