Towards Secured Service Provisioning for the Internet of Healthcare Things

Farhin, Fahiba; Kaiser, M. Shamim; Mahmud, Mufti

doi:10.1109/aict50176.2020.9368580

Cited by 38 publications

(8 citation statements)

References 75 publications

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“…In recent years artificial intelligence (AI) has been applied in diverse problem domains to solve various challenging problems, including text classification [13,14,15,16], cyber security [17,18,19,20], neurological disease detection [21,22,23,12] and management [24,25,26,27,28,29], elderly care [30,31], fighting pandemic [32,33,34,35,36,37,38], and healthcare service delivery [39,40,41]. In particular, deep learning (DL) has attracted a lot of attention [6,7].…”

Section: Related Workmentioning

confidence: 99%

Four-Way Classification of Alzheimer's Disease using Deep Siamese Convolutional Neural Network with Triplet-Loss Function

Hajamohideen

Shaffi

Mahmud

et al. 2022

Preprint

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Alzheimer's disease (AD) is a neurodegenerative disease that causes irreversible damage to several brain regions, including the hippocampus causing impairment in cognition, function, and behaviour. Early diagnosis of the disease will reduce the suffering of the patients and their family members. Towards this aim, in this paper, we propose a Siamese Convolutional Neural Network (CNN) architecture that employs the triplet-loss function for the representation of input MRI images as k-dimensional embeddings. We used both pre-trained and non-pre-trained CNNs to transform images into the embedding space. These embeddings are subsequently used for the 4-way classification of Alzheimer's Disease. The model efficacy was tested using the ADNI and OASIS datasets which produced an accuracy of 91.83% and 93.85%, respectively. Furthermore, obtained results are compared with similar methods proposed in the literature.

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Section: Related Workmentioning

confidence: 99%

Four-Way Classification of Alzheimer's Disease using Deep Siamese Convolutional Neural Network with Triplet-Loss Function

Hajamohideen

Shaffi

Mahmud

et al. 2022

Preprint

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“…10 Performance comparison of all the three previous works with the Proposed EHIDF Now for best strategies, the benefits will be maximized Be D , Be A and for that, it is differentiated with the probabilities, defined by Eqs. (8) and (9) .…”

Section: Security Analysismentioning

confidence: 99%

“…It extends the capabilities of cloud computing and IT services at the edge of the cellular network. This technology was primarily based MEC technology is specially designed for compute-intensive and latency-sensitive applications like augmented reality [3], real-time data analysis [4], Internet of Things (IoT) [5], video analytics, indoor positing, smart vehicles [6], smart IoT-based healthcare system [7][8][9][10], and image processing.…”

Section: Introductionmentioning

confidence: 99%

An edge based hybrid intrusion detection framework for mobile edge computing

Singh

Chatterjee

Satapathy

2021

Complex Intell. Syst.

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The Mobile Edge Computing (MEC) model attracts more users to its services due to its characteristics and rapid delivery approach. This network architecture capability enables users to access the information from the edge of the network. But, the security of this edge network architecture is a big challenge. All the MEC services are available in a shared manner and accessed by users via the Internet. Attacks like the user to root, remote login, Denial of Service (DoS), snooping, port scanning, etc., can be possible in this computing environment due to Internet-based remote service. Intrusion detection is an approach to protect the network by detecting attacks. Existing detection models can detect only the known attacks and the efficiency for monitoring the real-time network traffic is low. The existing intrusion detection solutions cannot identify new unknown attacks. Hence, there is a need of an Edge-based Hybrid Intrusion Detection Framework (EHIDF) that not only detects known attacks but also capable of detecting unknown attacks in real time with low False Alarm Rate (FAR). This paper aims to propose an EHIDF which is mainly considered the Machine Learning (ML) approach for detecting intrusive traffics in the MEC environment. The proposed framework consists of three intrusion detection modules with three different classifiers. The Signature Detection Module (SDM) uses a C4.5 classifier, Anomaly Detection Module (ADM) uses Naive-based classifier, and Hybrid Detection Module (HDM) uses the Meta-AdaboostM1 algorithm. The developed EHIDF can solve the present detection problems by detecting new unknown attacks with low FAR. The implementation results illustrate that EHIDF accuracy is 90.25% and FAR is 1.1%. These results are compared with previous works and found improved performance. The accuracy is improved up to 10.78% and FAR is reduced up to 93%. A game-theoretical approach is also discussed to analyze the security strength of the proposed framework.

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“…In recent years artificial intelligence (AI) has been applied in diverse problem domains to solve various challenging problems including student engagement [10], virtual reality exposure therapy [11], text classification [12,13,14,15], cyber security [16,17,18,19], neurological disease detection [20,21,22] and management [23,24,25,26,27,28], elderly care [29,30], biological data mining [31,32], fighting pandemic [33,34,35,36,37,38,39], and healthcare service delivery [40,41,42]. In particular, convolutional neural network (CNN) has recently accomplished remarkable achievements in Pathology Informatics [43,44,45] and Bioinformatics [46,47,48], suggesting that CNN has the ability to solve these challenges.…”

Section: Introductionmentioning

confidence: 99%

Detection of Osteosarcoma using Deep Feature Extraction with Federated Learning and MLP from Histopathological Images

Chowdhury,

Mahmud,

Elahe

et al. 2023

Preprint

Self Cite

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Background: The most common type of cancerous bone tumour is Osteosarcoma. Microscopic analysis of coloured slides is mostly used for Osteosarcoma diagnosis. As Osteosarcoma is highly heterogeneous, pathologists struggle to get a consensus diagnosis. Moreover, manual analysis of microscopic images is laborious, time-consuming, and vulnerable to bias. Method: In this research, a framework that extracts features based on CNN is developed to predict Osteosarcoma to address previous studies' performance gaps and shortcomings. The dataset used for this experiment is obtained from pathology archives at Children's Medical Center in Dallas, which includes 1144 histology glass slides. First, data augmentation is used to extend the dataset size. This study found Blur, Contrast, and Random augmentation perform well for the Osteosarcoma dataset. Next, we primarily considered six pre-trained transfer learning (TL) models and modified the models to extract features. Then, we examined feature selectors like Genetic Algorithm, Principal Component Analysis, Recursive Feature Elimination and Baruto and found PCA selects the optimal feature set. Finally, we fed the selected features into a fine-tuned MLP called Grid search MLP (GsMLP) and utilised federated learning (FL) to ensure the diversity and privacy of the dataset. Results: Experiments have revealed that the modified Xception model performs better in extracting features for Osteosarcoma prediction. Also, the proposed framework performed better than state-of-the-art techniques such as Convolutional Neural Network, VGG19, Random Forest, and AdaBoost with 98.20% accuracy and 97.5% Receiver Operating Characteristic - Area Under the Curve.

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Towards Secured Service Provisioning for the Internet of Healthcare Things

Cited by 38 publications

References 75 publications

Four-Way Classification of Alzheimer's Disease using Deep Siamese Convolutional Neural Network with Triplet-Loss Function

Four-Way Classification of Alzheimer's Disease using Deep Siamese Convolutional Neural Network with Triplet-Loss Function

An edge based hybrid intrusion detection framework for mobile edge computing

Detection of Osteosarcoma using Deep Feature Extraction with Federated Learning and MLP from Histopathological Images

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