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

Efficient evaluation for machine learning (ML)-based intrusion detection systems (IDSs) for federated learning (FL) in the Internet of Medical Things (IoMTs) environment falls under the standardisation and multicriteria decision-making (MCDM) problems. Thus, this study is developing an MCDM framework for standardising and benchmarking the ML-based IDSs used in the FL architecture of IoMT applications. In the methodology, firstly, the evaluation criteria of ML-based IDSs are standardised using the fuzzy Delphi method (FDM). Secondly, the evaluation decision matrix (DM) is formulated based on the intersection of standardised evaluation criteria and a list of ML-based IDSs. Such formulation is achieved using a dataset with 125,973 records, and each record comprises 41 features. Thirdly, the integration of MCDM methods is formulated to determine the importance weights of the main and sub standardised security and performance criteria, followed by benchmarking and selecting the optimal ML-based IDSs. In this phase, the Borda voting method is used to unify the different ranks and perform a group benchmarking context. The following results are confirmed. (1) Using FDM, 17 out of 20 evaluation criteria (14 for security and 3 for performance) reach the consensus of experts. (2) The area under curve criterion has the lowest set of weights, whilst the CPU time criterion has the highest one. (3) VIKOR group ranking shows that the BayesNet is a best classifier, whilst SVM is the last choice. For evaluation, three assessments, namely, systematic ranking, computational cost and comparative analysis, are used.

Based on T-spherical fuzzy environment: A combination of FWZIC and FDOSM for prioritising COVID-19 vaccine dose recipients.

The problem complexity of multi-criteria decision-making (MCDM) has been raised in the distribution of coronavirus disease 2019 (COVID-19) vaccines, which required solid and robust MCDM methods. Compared with other MCDM methods, the fuzzy-weighted zero-inconsistency (FWZIC) method and fuzzy decision by opinion score method (FDOSM) have demonstrated their solidity in solving different MCDM challenges. However, the fuzzy sets used in these methods have neglected the refusal concept and limited the restrictions on their constants. To end this, considering the advantage of the T-spherical fuzzy sets (T-SFSs) in handling the uncertainty in the data and obtaining information with more degree of freedom, this study has extended FWZIC and FDOSM methods into the T-SFSs environment (called T-SFWZIC and T-SFDOSM) to be used in the distribution of COVID-19 vaccines. The methodology was formulated on the basis of decision matrix adoption and development phases. The first phase described the adopted decision matrix used in the COVID-19 vaccine distribution. The second phase presented the sequential formulation steps of T-SFWZIC used for weighting the distribution criteria followed by T-SFDOSM utilised for prioritising the vaccine recipients. Results revealed the following: (1) T-SFWZIC effectively weighted the vaccine distribution criteria based on several parameters including T = 2, T = 4, T = 6, T = 8, and T = 10. Amongst all parameters, the age criterion received the highest weight, whereas the geographic locations severity criterion has the lowest weight. (2) According to the T parameters, a considerable variance has occurred on the vaccine recipient orders, indicating that the existence of T values affected the vaccine distribution. (3) In the individual context of T-SFDOSM, no unique prioritisation was observed based on the obtained opinions of each expert. (4) The group context of T-SFDOSM used in the prioritisation of vaccine recipients was considered the final distribution result as it unified the differences found in an individual context. The evaluation was performed based on systematic ranking assessment and sensitivity analysis. This evaluation showed that the prioritisation results based on each T parameter were subject to a systematic ranking that is supported by high correlation results over all discussed scenarios of changing criteria weights values.