An intrusion detection system (IDS) is a crucial element in cyber security concerns. IDS is a safeguarding module that is designed to identify unauthorized activities in network environments. The importance of constructing IDSs has never been this significant with the growing number of attacks on network layers. This research work was intended to draw the attention of the authors to a different aspect of intrusion detection, considering privacy and the contribution of the features on attack classes. At present, the majority of the existing IDSs are designed based on centralized infrastructure, which raises serious concerns about security as the network data from one system are exposed to another system. This act of sharing the original network data with another server can worsen the current arrangement of protecting privacy within the network. In addition, the existing IDS models are merely a tool for identifying the attack categories without analyzing a further emphasis of the network feature on the attacks. In this article, we propose a novel framework, FEDXAIIDS, converging federated learning and explainable AI. The proposed approach enables IDS models to be collaboratively trained across multiple decentralized devices while ensuring that local data remain securely on edge nodes, thus mitigating privacy risks. The primary objectives of the proposed study are to reveal the privacy concerns of centralized systems and identify the most significant features to comprehend the contribution of the features to the final output. Our proposed model was designed, fusing federated learning (FL) with Shapley additive explanations (SHAPs), using an artificial neural network (ANN) as a local model. The framework has a server device and four client devices that have their own data set on their end. The server distributes the primary model constructed using an ANN among the local clients. Next, the local clients train their individual part of the data set, deploying the distributed model from the server, and they share their feedback with the central end. The central end then incorporates an aggregator model named FedAvg to assemble the separate results from the clients into one output. At last, the contribution of the ten most significant features is evaluated by incorporating SHAP. The entire research work was executed on CICIoT2023. The data set was partitioned into four parts and distributed among the four local ends. The proposed method demonstrated efficacy in intrusion detection, achieving 88.4% training and 88.2% testing accuracy. Furthermore, UDP has been found to be the most significant feature of the network layer from the SHAP analysis. Simultaneously, the incorporation of federated learning has ensured the safeguarding of the confidentiality of the network information of the individual ends. This enhances transparency and ensures that the model is both reliable and interpretable. Federated XAI IDS effectively addresses privacy concerns and feature interpretability issues in modern IDS frameworks, contributing to the advancement of secure, interpretable, and decentralized intrusion detection systems. Our findings accelerate the development of cyber security solutions that leverage federated learning and explainable AI (XAI), paving the way for future research and practical implementations in real-world network security environments.

Blockchain is an emerging technology that is being used to create innovative solutions in many areas, including healthcare. Nowadays healthcare systems face challenges, especially with security, trust, and remote data access. As patient records are digitized and medical systems become more interconnected, the risk of sensitive data being exposed to cyber threats has grown. In this evolving time for healthcare, it is important to find a balance between the advantages of new technology and the protection of patient information. The combination of blockchain–InterPlanetary File System technology and conventional electronic health record (EHR) management has the potential to transform the healthcare industry by enhancing data security, interoperability, and transparency. However, a major issue that still exists in traditional healthcare systems is the continuous problem of remote data unavailability. This research examines practical methods for safely accessing patient data from any location at any time, with a special focus on IPFS servers and blockchain technology in addition to group signature encryption. Essential processes like maintaining the confidentiality of medical records and safe data transmission could be made easier by these technologies. Our proposed framework enables secure, remote access to patient data while preserving accessibility, integrity, and confidentiality using Ethereum blockchain, IPFS, and group signature encryption, demonstrating hospital-scale scalability and efficiency. Experiments show predictable throughput reduction with file size (200 → 90 tps), controlled latency growth (90 → 200 ms), and moderate gas increase (85k → 98k), confirming scalability and efficiency under varying healthcare workloads. Unlike prior blockchain–IPFS–encryption frameworks, our system demonstrates hospital-scale feasibility through the practical integration of group signatures, hierarchical key management, and off-chain erasure compliance. This design enables scalable anonymous authentication, immediate blocking of compromised credentials, and efficient key rotation without costly re-encryption.

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Counterfeit pharmaceuticals, fragmented data management, and the absence of transparent verification mechanisms continue to threaten the integrity of modern healthcare drug supply chains. Although blockchain-based traceability frameworks improve decentralization and auditability, existing approaches frequently lack formally verified authentication guarantees and interpretable security decision mechanisms. To address these limitations, this paper proposes a unified blockchain-enabled healthcare supply-chain framework integrating nonce-based cryptographic authentication, formal security verification, Solidity smart contracts, and SHAP-based explainable artificial intelligence (XAI). The proposed protocol employs elliptic-curve digital signatures, nonce-based challenge–response authentication, and blockchain validation mechanisms to ensure secure communication among manufacturers, distributors, pharmacies, and regulatory authorities. Formal verification using ProVerif under the Dolev–Yao adversarial model confirms secrecy, authentication, integrity, and replay-attack resistance properties, with no executable attack traces detected. A permissioned Ethereum test network and Python-based cryptographic simulation environment were implemented using Solidity, Web3.py, and Py-EVM to evaluate deployment feasibility. Additional deployment validation was performed through Ethereum Sepolia test-network execution and Mythril smart-contract security auditing. Experimental results demonstrate stable and scalable operational performance, achieving average transaction latency below 3 s, throughput performance comparable to recent healthcare blockchain systems reporting approximately 95–155 transactions per second, and average cryptographic signing time of 0.71 ms per transaction. Gas-consumption analysis reported a mean transaction cost of 182,345 gas units with low execution variance, while storage overhead exhibited near-linear scalability as transaction volume increased. Additional adversarial evaluation demonstrated resilient transaction-validation behavior under varying node participation and simulated communication disruptions. To improve interpretability and regulatory transparency, the framework integrates SHAP-based explainable verification capable of identifying the relative influence of signature validity, nonce freshness, blockchain confirmation, and integrity verification on transaction acceptance decisions. Ablation analysis additionally showed that the complete integrated framework achieved 96.8% security assurance, 94.5% interpretability, and 97.2% traceability performance, substantially outperforming partial configurations lacking blockchain, formal verification, or explainability modules. The principal contribution of this work lies in combining formally verified authentication, blockchain-enabled traceability, explainable security analytics, and deployment-oriented validation within a unified pharmaceutical supply-chain architecture suitable for secure, scalable, and regulation-aware healthcare environments.