Intrusion Detection Systems (IDS) are crucial module of cybersecurity which is designed to identify unauthorized activities in network environments. Traditional IDS, on the other hand, have a number of problems, such as high rates of inaccurate positives and inaccurate negatives and a lack of explainability that makes it difficult to provide adequate protection. Furthermore, centralized IDS approaches have issues with interpretability and data protection, especially when dealing with sensitive data. In order to overcome these drawbacks, we provide Federated XAI IDS, a brand-new explainable and privacy-preserving IDS that improves security and interpretability by fusing Federated Learning (FL) with Shapley Additive Explanations (SHAP). Our approach enables IDS models to be collaboratively trained across multiple decentralized devices while ensuring that local data remains securely on edge nodes, thus mitigating privacy risks. The Artificial Neural Network (ANN)-based IDS is distributed across four clients in a federated setup using the CICIoT2023 dataset, with model aggregation performed via FedAvg. The proposed method demonstrated efficacy in intrusion detection, achieving 88.4% training and 88.2% testing accuracy. Furthermore, SHAP was utilized to analyze feature importance, providing a deeper comprehension of the critical attributes influencing model predictions. Transparency is improved and the model becomes more dependable and interpretable thanks to the feature importance ranking that SHAP produces. Our findings demonstrate how well Federated XAI IDS handles the two problems of explainability and privacy in intrusion detection. This dissertation accelerates the major establishment in the creation of safe, interpretable, and decentralized intrusion detection systems (IDS) for contemporary cybersecurity applications by utilizing federated learning and explainable AI (XAI).

This study addresses the impact of movement restrictions, particularly during pandemics, on global consumer behavior, with a focus on grocery shopping. Despite the widespread effects, research on these changes remains limited, prompting the need to investigate hidden purchase patterns contributing to economic growth. Employing secondary data analysis, the study explores the effects of movement restrictions on customer consumption patterns, using K-means cluster analysis to identify distinct consumer segments. The findings highlight significant impacts on purchasing power, item prices, and consumption behavior, with a notable increase in item prices during the post-restriction period followed by a subsequent decline influenced by economic factors such as financial uncertainty and shifting priorities. This research contributes by shedding light on the dynamic nature of consumer behavior during and after movement restrictions, offering valuable insights for policymakers and businesses navigating the post-restriction economic landscape and informing strategies for economic recovery and growth.