Enormous risks and hidden dangers of information security exist in the applications of Internet of Things (IoT) technologies. To secure IoT software systems, software engineers have to deploy advanced security software such as Intrusion Detection Systems (IDS) that are able to keep track of how the IoT devices behave within the network and detect any malicious activity that may be occurring. Considering that IoT devices generate large amounts of data, Artificial Intelligence (AI) is often regarded as the best method for implementing IDS, thanks to AI’s high capability in processing large amounts of IoT data. To tackle these security concerns, specifically the ones tied to the privacy of data used in IoT systems, the software implementation of a Federated Learning (FL) method is often used to improve both privacy preservation (PP) and scalability in IoT networks. In this article, we present an FL IDS that leverages a 1-Dimensional Convolutional Neural Network (CNN) for efficient and accurate intrusion detection in IoT networks. To address the critical issue of PP in FL, we incorporate three techniques: Differential Privacy, Diffie–Hellman Key Exchange, and Homomorphic Encryption. To evaluate the effectiveness of our solution, we conduct experiments on seven publicly available IoT datasets: TON-IoT, IoT-23, BoT-IoT, CIC IoT 2023, CIC IoMT 2024, RT-IoT 2022, and EdgeIIoT. Our CNN-based approach achieves outstanding performance with an average accuracy, precision, recall, and F1-score of 97.31%, 95.59%, 92.43%, and 92.69%, respectively, across these datasets. These results demonstrate the effectiveness of our approach in accurately identifying and detecting intrusions in IoT networks. Furthermore, our experiments reveal that implementing all three PP techniques only incurs a minimal increase in computation time, with a 10% overhead compared to our solution without any PP mechanisms. This finding highlights the feasibility and efficiency of our solution in maintaining privacy while achieving high performance. Finally, we show the effectiveness of our solution through a comparison study with other recent IDS trained and tested on the same datasets we use.

Unprecedented and aggressive data access and transmission practices employed by vendors and mobile app developers have aggravated mobile app users’ privacy invasion and privacy concerns. Mobile apps increasingly collect data about users’ behaviour, personal preferences, location, and other personally-identifying information. Often, these data are collected without users’ permission, are unnecessary for the mobile app’s functionality, and are used for unauthorised purposes. We propose a comprehensive but parsimonious research model by drawing upon the protection motivation theory, the theory of planned behaviour, and personality traits literature. The study empirically examines the factors that influence mobile app users’ privacy concerns and how these concerns drive users’ intention and behaviour to use mobile apps. The findings indicate that the threat appraisal has a strong effect on users’ privacy concerns and the personality trait of neuroticism has a substantial effect on perceived vulnerability and perceived severity. Results also indicate that privacy protection behaviours are influenced by users’ coping appraisal and intention to protect their privacy. Our work contributes to the privacy protection behaviour and mobile app privacy literature. We discuss the implications of the results for researchers and practitioners.

Purpose This study aims to investigate the threat phenomenon as perceived by Internet of Things (IoT) users and examines the role of anxiety, desire and personal innovativeness in the behavioral intention toward the usage of IoT devices. Design/methodology/approach A unified research model is developed based on the protection motivation theory, theory of reasoned action, theory of self-regulation and the review of relevant theoretical, empirical and practitioner literature. Data were collected from 315 assistive IoT device users and analyzed using partial least squares structural modeling. Findings The results indicate strong support for the proposed research model. All relationships, except one, were significant at the 0.05 level. Desire was found to play a direct as well as a moderating role between fear and behavioral intention to continue using assistive IoT devices, which was also directly influenced by anxiety and personal innovativeness. Research limitations/implications Understanding the security behaviors of IoT users will help researchers and practitioners develop preventive measures and robust security solutions for the IoT devices to avert any threats from cyber-attacks and to boost users’ confidence levels. Future research will benefit from replicating the study using longitudinal data. Originality/value This study is one of the first studies that integrate multiple perspectives to present a holistic research model. To the authors’ knowledge, anxiety, desire and personal innovativeness, key factors influencing fear and behavioral intention, have not been studied in the domain of adoption of IoT assistive devices. Additionally, the study offers a new dimension to IoT users’ security behaviors.

Research and practice on knowledge management (KM) have shown that information technology alone cannot guarantee that employees will volunteer and share knowledge. While previous studies have linked motivational factors to knowledge sharing (KS), we took a further step to thoroughly examine this theoretically and empirically. We developed a unified model that is comprehensive and yet parsimonious, based on the decomposed theory of planned behaviour (DTPB) with three sets of critical antecedents: psychological, organisational and technological that are theorised to influence KS behaviours. Results of a field survey of knowledge workers support the majority of hypothesised relationships, and explained 41.3% of the variance in the actual KS behaviours and 60.8% of the variance in the intention to share knowledge. These results far exceed the predictive powers achieved by previous studies. Among our significant findings include a strong positive influence of perceived enjoyment in helping others (PEH) and a strong negative influence of perceived loss of knowledge power (PLK). Based on the findings, we discussed the study's implications for research and practice.