This research introduces a comprehensive collaborative intrusion detection system (CIDS) framework aimed at bolstering the security of Internet of Things (IoT) environments by synergistically integrating lightweight architecture, trust management, and privacy-preserving mechanisms. The proposed hierarchical architecture spans edge, fog, and cloud layers, ensuring efficient and scalable collaborative intrusion detection. Trustworthiness is established through the incorporation of distributed ledger technology (DLT), leveraging blockchain frameworks to enhance the reliability and transparency of communication among IoT devices. Furthermore, the research adopts federated learning (FL) techniques to address privacy concerns, allowing devices to collaboratively learn from decentralized data sources while preserving individual data privacy. Validation of the proposed approach is conducted using the CICIoT2023 dataset, demonstrating its effectiveness in enhancing the security posture of IoT ecosystems. This research contributes to the advancement of secure and resilient IoT infrastructures, addressing the imperative need for lightweight, trust-managing, and privacy-preserving solutions in the face of evolving cybersecurity challenges. According to our experiments, the proposed model achieved an average accuracy of 97.65%, precision of 97.65%, recall of 100%, and F1-score of 98.81% when detecting various attacks on IoT systems with heterogeneous devices and networks. The system is a lightweight system when compared with traditional intrusion detection that uses centralized learning in terms of network latency and memory consumption. The proposed system shows trust and can keep private data in an IoT environment.

One of the most visible manifestations of the changes brought about by the digitization of everyday life is undoubtedly the spread of electronic commerce. It is difficult to think of the digital economy without considering transactions through electronic channels. In turn, the user interface (UI) is the key to e-commerce, as it is usually the first and primary point of contact between business and consumer. A key trend in e-commerce is the personalization of communications, which can improve the user experience, increase customer satisfaction and deliver tangible business benefits. Today, it is technically possible to base this personalization on an analysis of user behavior using artificial intelligence and machine learning techniques. A common form of personalization in e-commerce is the use of product recommendation systems, but the user interface can be tailored much more extensively. The approach described and discussed in this paper is a multivariant user interface that allows the layout to be tailored to the characteristics, attributes, and behaviors of customer groups generated using machine learning techniques. The results of the research carried out make it possible to verify the practicality of the proposed solution and provide an opportunity to identify development directions that take into account the potential of artificial intelligence. The application of the concept described in the paper is broad, covering all aspects of e-commerce design that require compromises when serving a single UI variant, but allow flexibility and customization for different users when serving a multivariant UI.

In this paper a few methods for anomaly detection in computer networks with the use of time series methods are proposed. The special interest was put on Brown's exponential smoothing, seasonal decomposition, naive forecasting and Exponential Moving Average method. The validation of the anomaly detection methods has been performed using experimental data sets and statistical analysis which has shown that proposed methods can efficiently detect unusual situations in network traffic. This means that time series methods can be successfully used to model and predict a traffic in computer networks as well as to detect some unusual or unrequired events in network traffic.

Detecting coordinated attacks in cybersecurity is challenging due to their sophisticated and distributed nature, making traditional Intrusion Detection Systems often ineffective, especially in heterogeneous networks with diverse devices and systems. This research introduces a novel Collaborative Intrusion Detection System (CIDS) using a Weighted Ensemble Averaging Deep Neural Network (WEA-DNN) designed to detect such attacks. The WEA-DNN combines deep learning techniques and ensemble methods to enhance detection capabilities by integrating multiple Deep Neural Network (DNN) models, each trained on different data subsets with varying architectures. Differential Evolution optimizes the model’s contributions by calculating optimal weights, allowing the system to collaboratively analyze network traffic data from diverse sources. Extensive experiments on real-world datasets like CICIDS2017, CSE-CICIDS2018, CICToNIoT, and CICBotIoT show that the CIDS framework achieves an average accuracy of 93.8%, precision of 78.6%, recall of 60.4%, and an F1-score of 62.4%, surpassing traditional ensemble models and matching the performance of local DNN models. This demonstrates the practical benefits of WEA-DNN in improving detection capabilities in real-world heterogeneous network environments, offering superior adaptability and robustness in handling complex attack patterns.

The continuous development and increasing availability of Internet of Things (IoT) solutions have led to an era of connectivity in which everyday objects—from household appliances to industrial machines—are connected via the Internet

Prevention and diagnosis of frailty syndrome (FS) in patients with heart failure (HF) require innovative systems to help medical personnel tailor and optimize their treatment and care. Traditional methods of diagnosing FS in patients could be more satisfactory. Healthcare personnel in clinical settings use a combination of tests and self-reporting to diagnose patients and those at risk of frailty, which is time-consuming and costly. Modern medicine uses artificial intelligence (AI) to study the physical and psychosocial domains of frailty in cardiac patients with HF. This paper aims to present the potential of using the AI approach, emphasizing machine learning (ML) in predicting frailty in patients with HF. Our team reviewed the literature on ML applications for FS and reviewed frailty measurements applied to modern clinical practice. Our approach analysis resulted in recommendations of ML algorithms for predicting frailty in patients. We also present the exemplary application of ML for FS in patients with HF based on the Tilburg Frailty Indicator (TFI) questionnaire, taking into account psychosocial variables.