Federated Learning for IoMT Applications: A Standardization and Benchmarking Framework of Intrusion Detection Systems

Alamleh, Amneh; Albahri, O. S.; Zaidan, A. A.; Albahri, A. S.; Alamoodi, A. H.; Zaidan, B. B.; Qahtan, Sarah; Al-Samarraay, Mohammed S.; Jasim, Ali Najm

doi:10.1109/jbhi.2022.3167256

Cited by 31 publications

(10 citation statements)

References 56 publications

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“…When choosing a platform [27], it can be essential to consider one that's open and flexible enough to support a security program as it changes. Consider whether it offers Open standards, Open-source technology, or Open connections.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation and Selection of a Cybersecurity Platform ─ Case of the Power Sector in India

Verma,

Koul,

2023

Decis. Mak. Appl. Manag. Eng.

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Maintaining interconnected infrastructures such as transportation, communication, power grids, and pipeline networks is paramount in emerging economies. One of the critical interruptions is the targeted attacks on the operating cyber-physical systems to disconnect operations, inspection, or monitoring of the system. Therefore, adopting a cybersecurity system (or platform) that provides holistic protection is vital for protecting the integrity of critical infrastructure networks. As such, this research aspires to provide a decision support system for cybersecurity managers or practitioners (in the Indian power sector) to select the best and appropriate platform for protection against cyber-attacks. A three-phase method is adopted. First, a literature search followed by an expert panel discussion identified alternatives (cybersecurity platforms) and selection criteria. Next, a questionnaire was developed. Thirdly, a hybrid Best-Worst Improved and COmprehensive distance-Based RAnking (BWM-I and COBRA) method was proposed and applied to evaluate the cybersecurity platform alternatives. Four alternatives (Cloud-Based Platforms, Web-Based Platforms, Application-Based Platforms, and AI-Based Platforms), six primary criteria, and fifteen unique sub-criteria were identified. Responses were collected from 80 power utility managers on a pan-India basis, ranking "End-to-End Coverage" criteria and the AI-Based platform as best. This approach identified the best cybersecurity platform that, if adopted, can be extended to other critical infrastructures, with an appropriate adjustment in the selection criteria. The study can be helpful to practitioners in evaluating cybersecurity platforms. Furthermore, it addresses a research gap in its application in a developing country like India.

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Section: Introductionmentioning

confidence: 99%

“…An open platform connects to third-party tools and supports custom connections and development. As per [27], this approach can help reduce vendor lock-in and promote interoperability with multiple security and Information Technology (IT) tools.…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Selection of a Cybersecurity Platform ─ Case of the Power Sector in India

Verma,

Koul,

2023

Decis. Mak. Appl. Manag. Eng.

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“…Weighting criteria can be done objectively or subjectively 32–48 . In objective weighting methods such as entropy, the criterion importance is computed using raw data; in this case, changes in the raw data affect the weight value's accuracy 49–52 . Moreover, subjective weighting methods reflect the subjective judgement of the decision‐makers/experts 53 .…”

Section: Introductionmentioning

confidence: 99%

“…[32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48] In objective weighting methods such as entropy, the criterion importance is computed using raw data; in this case, changes in the raw data affect the weight value's accuracy. [49][50][51][52] Moreover, subjective weighting methods reflect the subjective judgement of the decision-makers/experts. 53 Numerous subjective weighting methods have been proposed; however, when it comes to weighting criteria, the analytic hierarchy process (AHP) 54 and best-worst method (BWM) 55 methods have a high success rate.…”

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confidence: 99%

Towards physician's experience: Development of machine learning model for the diagnosis of autism spectrum disorders based on complex T‐spherical fuzzy‐weighted zero‐inconsistency method

Albahri

Zaidan

Alsattar³

et al. 2022

Computational Intelligence

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Autism spectrum disorders (ASD) are a diverse group of conditions characterized by difficulty with social interaction and communication. ASD is expected to be a high‐risk disease. Recent studies have focused on the diagnosis based on sociodemographic and family characteristics factors. The development of a diagnosis model, which is primarily based on machine learning methods, has been carried out to alleviate the detection of autism. However, they neglected the importance of ASD features in a training dataset, especially because some features have different levels of contributions to the processing data and possess more relevancies to the classification information than others. Such limitations use preprocessing techniques for the construction of the machine learning model, but the role of the physician's experience towards feature contributions remains limited. However, for certain autism datasets, the relevancies of sociodemographic and family characteristic feature concerning the given class labels should be considered. Accordingly, this study developed a new machine learning model for the diagnosis of ASD based on multi‐criteria decision‐making (MCDM). By using three methodology phases, the model combines two representative theories, namely, MCDM and machine learning. The identification phase for imbalance ASD dataset and application of pre‐possessing stages by imputing missing values, feature selection of sociodemographic and family characteristics, and data imbalanced approach resulted in balanced ASD dataset, including 107,573 cases. The development phase for the new model was achieved by the proposed complex T‐spherical fuzzy‐weighted zero‐inconsistency (CT‐SFWZIC) method. CT‐SFWZIC was developed based on a new fuzzy set (i.e., complex T‐spherical fuzzy) for weighting affected features, and then applied for training and testing the machine learning model considering various complex T‐spherical fuzzy membership functions (i.e., T = 1, 2, 3, 5, 7, and 10). The results obtained from a 10‐fold cross‐validation test for all T values by using nine machine learning classifiers were measured under seven evaluation metrics, namely AUC, accuracy, F1, precision, recall, training time (s), and test time (s). Performance evaluation results reveal that AdaBoost can be used to boost the ASD diagnosis as the best machine learning algorithm for all T values based on all metrics to improve the diagnosis based on physician's assessment. Under the most extreme evaluation metric, which is accuracy, the results of the AdaBoost classifiers for T = 1, 2, 3, 5, 7, 10 have obtained 0.99948, 0.99934, 0.99930, 0.99939, 0.99910, and 0.99930, respectively.

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“…This type of research is most suited for this case study, because it deals with the main issues, such as the availability of many criteria (each disease has its own criteria), trade-offs and data variation [25][26][27][28][29][30][31][32]. Addressing all these issues requires a robust MCDM method [21,[33][34][35][36][37][38][39][40][41][42][43][44]. An examination of the literature shows that various techniques were proposed, and many of them are unique [23,24,[45][46][47][48][49][50].…”

Section: Introductionmentioning

confidence: 99%

Hospital selection framework for remote MCD patients based on fuzzy q-rung orthopair environment

Alamoodi

Albahri

Zaidan

et al. 2022

Neural Comput & Applic

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This research proposes a novel mobile health-based hospital selection framework for remote patients with multi-chronic diseases based on wearable body medical sensors that use the Internet of Things. The proposed framework uses two powerful multi-criteria decision-making (MCDM) methods, namely fuzzy-weighted zero-inconsistency and fuzzy decision by opinion score method for criteria weighting and hospital ranking. The development of both methods is based on a Q-rung orthopair fuzzy environment to address the uncertainty issues associated with the case study in this research. The other MCDM issues of multiple criteria, various levels of significance and data variation are also addressed. The proposed framework comprises two main phases, namely identification and development. The first phase discusses the telemedicine architecture selected, patient dataset used and decision matrix integrated. The development phase discusses criteria weighting by q-ROFWZIC and hospital ranking by q-ROFDOSM and their sub-associated processes. Weighting results by q-ROFWZIC indicate that the time of arrival criterion is the most significant across all experimental scenarios with (0.1837, 0.183, 0.230, 0.276, 0.335) for (q = 1, 3, 5, 7, 10), respectively. Ranking results indicate that Hospital (H-4) is the best-ranked hospital in all experimental scenarios. Both methods were evaluated based on systematic ranking and sensitivity analysis, thereby confirming the validity of the proposed framework.

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Federated Learning for IoMT Applications: A Standardization and Benchmarking Framework of Intrusion Detection Systems

Cited by 31 publications

References 56 publications

Evaluation and Selection of a Cybersecurity Platform ─ Case of the Power Sector in India

Evaluation and Selection of a Cybersecurity Platform ─ Case of the Power Sector in India

Towards physician's experience: Development of machine learning model for the diagnosis of autism spectrum disorders based on complex T‐spherical fuzzy‐weighted zero‐inconsistency method

Hospital selection framework for remote MCD patients based on fuzzy q-rung orthopair environment

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