The Internet of Things (IoT) has emerged as a technology capable of connecting heterogeneous nodes/objects, such as people, devices, infrastructure, and makes our daily lives simpler, safer, and fruitful. Being part of a large network of heterogeneous devices, these nodes are typically resource-constrained and became the weakest link to the cyber attacker. Classical encryption techniques have been employed to ensure the data security of the IoT network. However, high-level encryption techniques cannot be employed in IoT devices due to the limitation of resources. In addition, node security is still a challenge for network engineers. Thus, we need to explore a complete solution for IoT networks that can ensure nodes and data security. The rule-based approaches and shallow and deep machine learning algorithms- branches of Artificial Intelligence (AI)- can be employed as countermeasures along with the existing network security protocols. This paper presented a comprehensive layer-wise survey on IoT security threats, and the AI-based security models to impede security threats. Finally, open challenges and future research directions are addressed for the safeguard of the IoT network.

The Internet-of-Things (IoT) is an emerging technology that connects and integrates a massive number of smart physical devices with virtual objects operating in diverse platforms through the internet. Due to massive size and physical spread of many applications such as smart healthcare, IoT is increasingly implemented in distributed setting. This distributed nature of implementation of the entities connected to the IoT networks are exposed to an unprecedented level of privacy and security threats. This is particularly severe for IoT healthcare system as it involves huge volume of sensitive and personal data. Although blockchain has posed to be the solution in this scenario thanks to its inherent distributed ledger technology (DLT), it suffers from a major drawback of rapidly increasing storage and computation requirements with the increase in network size which makes its implementation impractical. This paper proposes a holochain-based security and privacy-preserving framework for IoT healthcare systems that overcomes the scalability challenge and is particularly suited for resource constrained IoT scenarios. Through thorough analysis and performance results, we have demonstrated that the holochain based IoT healthcare solution outperforms blockchain based solution in terms of resource requirements while ensuring appropriate level of privacy and security.

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.

Brain tumors are among the most severe health challenges, necessitating early and precise diagnosis for effective treatment planning. This study introduces an optimized hybrid transfer learning (TL) framework for brain tumor classification using magnetic resonance imaging images. The proposed system integrates advanced preprocessing techniques, an ensemble of pretrained deep learning models, and explainable artificial intelligence (XAI) methods to achieve high accuracy and reliability. The methodology enhances image quality through noise reduction and contrast enhancement, facilitating robust feature extraction. The ensemble model combines VGG16 and ResNet152V2 architectures, achieving a classification accuracy of 99.47% on a challenging four‐class dataset. Additionally, gradient‐weighted class activation mapping and SHapley Additive exPlanations (SHAP)‐based XAI techniques provide visual and quantitative insights into model predictions, improving interpretability and clinical trust. This comprehensive framework demonstrates the potential of hybrid TL and XAI in advancing diagnostic accuracy and supporting clinical decision‐making for brain tumor detection. The results underscore its applicability in clinical settings, particularly in resource‐constrained environments.

The coronavirus (COVID-19) has appeared as the greatest challenge due to its continuous structural evolution as well as the absence of proper antidotes for this particular virus. The virus mainly spreads and replicates itself among mass people through close contact which unfortunately can happen in many unpredictable ways. Therefore, to slow down the spread of this novel virus, the only relevant initiatives are to maintain social distance, perform contact tracing, use proper safety gears, and impose quarantine measures. But despite being conceptually possible, these approaches are very difficult to uphold in densely populated countries and areas. Therefore, to control the virus spread, researchers and authorities are considering the use of smartphone based mobile applications (apps) to identify the likely infected persons as well as the highly risky zones to maintain isolation and lockdown measures. However, these methods heavily depend on advanced technological features and expose significant privacy loopholes. In this paper, we propose a new method for COVID-19 contact tracing based on mobile phone users’ geolocation data. The proposed method will help the authorities to identify the number of probable infected persons without using smartphone based mobile applications. In addition, the proposed method can help people take the vital decision of when to seek medical assistance by letting them know whether they are already in the list of exposed persons. Numerical examples demonstrate that the proposed method can significantly outperform the smartphone app-based solutions.