User, Usage and Usability: Redefining Human Centric Cyber Security

Exploring the Impact of Security Technologies on Mental Health: A Comprehensive Review

This comprehensive review explores the intricate relationship between security technologies and mental health. Security technologies, including physical security, cybersecurity, and surveillance measures, are integral components of our modern world, designed to protect individuals, organizations, and society from various threats. While they are vital in enhancing safety, they also have profound implications for mental well-being. The review delves into the positive impacts of security technologies, including their capacity to enhance personal safety, reduce anxiety and fear, and instill a sense of security. However, it also reveals the negative consequences, such as privacy invasion, surveillance-related stress, paranoia, and ethical concerns, which can erode mental health. User perception and trust are central to understanding how individuals experience security technologies. The review emphasizes the importance of ethical guidelines, user education, and technological advancements in mitigating negative impacts. By embracing an ethical-by-design approach, empowering users, and promoting public awareness, a balanced equilibrium between security and mental health can be achieved. The conclusion highlights the significance of ongoing research and interdisciplinary collaboration to navigate this intricate relationship effectively. By prioritizing ethical considerations and fostering a dialogue that values security and individual well-being, we can ensure a safer and more mentally healthy future in our technologically interconnected world.

Unraveling the Ethical Enigma: Artificial Intelligence in Healthcare

The integration of artificial intelligence (AI) into healthcare promises groundbreaking advancements in patient care, revolutionizing clinical diagnosis, predictive medicine, and decision-making. This transformative technology uses machine learning, natural language processing, and large language models (LLMs) to process and reason like human intelligence. OpenAI's ChatGPT, a sophisticated LLM, holds immense potential in medical practice, research, and education. However, as AI in healthcare gains momentum, it brings forth profound ethical challenges that demand careful consideration. This comprehensive review explores key ethical concerns in the domain, including privacy, transparency, trust, responsibility, bias, and data quality. Protecting patient privacy in data-driven healthcare is crucial, with potential implications for psychological well-being and data sharing. Strategies like homomorphic encryption (HE) and secure multiparty computation (SMPC) are vital to preserving confidentiality. Transparency and trustworthiness of AI systems are essential, particularly in high-risk decision-making scenarios. Explainable AI (XAI) emerges as a critical aspect, ensuring a clear understanding of AI-generated predictions. Cybersecurity becomes a pressing concern as AI's complexity creates vulnerabilities for potential breaches. Determining responsibility in AI-driven outcomes raises important questions, with debates on AI's moral agency and human accountability. Shifting from data ownership to data stewardship enables responsible data management in compliance with regulations. Addressing bias in healthcare data is crucial to avoid AI-driven inequities. Biases present in data collection and algorithm development can perpetuate healthcare disparities. A public-health approach is advocated to address inequalities and promote diversity in AI research and the workforce. Maintaining data quality is imperative in AI applications, with convolutional neural networks showing promise in multi-input/mixed data models, offering a comprehensive patient perspective. In this ever-evolving landscape, it is imperative to adopt a multidimensional approach involving policymakers, developers, healthcare practitioners, and patients to mitigate ethical concerns. By understanding and addressing these challenges, we can harness the full potential of AI in healthcare while ensuring ethical and equitable outcomes.

Functional Security and Trust in Ultra-Connected 6G Ecosystem

Security and trust are the entangled role players in the future generation of wireless networks. Security in 5G networks is currently supported using several functions. Given the advantages of such a system, this article explores the functional security and trust for the 6G ecosystem with ultra-connectivity. Several associated challenges, application-specific domains, and consumer issues related to 6G security are discussed. The article highlights the network security-by-design and trust-by-design principles and performance expectations from the security protocols in supporting handover in an ultra-connected scenario. Finally, potential research directions are presented for a road towards the 6G ecosystem.

Integrating Human Factors in the Visualisation of Usable Transparency for Dynamic Risk Assessment

Modern technology and the digitisation era accelerated the pace of data generation and collection for various purposes. The orchestration of such data is a daily challenge faced by even experienced professional users in the context of Internet of Things (IoT)-enabled environments, especially when it comes to cybersecurity and privacy risks. This article presents the application of a user-centric process for the visualisation of automated decision making security interventions. The user interface (UI) development was guided by iterative feedback collection from user studies on the visualisation of a dynamic risk assessment (DRA)-based security solution for regular lay users. The methodology we applied starts with the definition of the methodological process to map possible technical actions to related usable actions. The definition and refinement of the user interface (UI) was controlled by the survey feedback loop from end user studies on their general technological knowledge, experience with smart homes, cybersecurity awareness and privacy preservation needs. We continuously improved the visualisation interfaces for configuring a cybersecurity solution and adjusting usable transparency of the control and monitoring of the dynamic risk assessment (DRA). For this purpose, we have designed, developed and validated a decision tree workflow and showed the evolution of the interfaces through various stages of the real-life trials executed under European H2020 project GHOST.

Usability and Security Testing of Online Links: A Framework for Click-Through Rate Prediction Using Deep Learning

The user, usage, and usability (3U’s) are three principal constituents for cyber security. The effective analysis of the 3U data using artificial intelligence (AI) techniques allows to deduce valuable observations, which allow domain experts to design practical strategies to alleviate cyberattacks and ensure decision support. Many internet applications, such as internet advertising and recommendation systems, rely on click-through rate (CTR) prediction to anticipate the possibility that a user would click on an ad or product, which is key for understanding human online behaviour. However, online systems are prone to click on fraud attacks. We propose a Human-Centric Cyber Security (HCCS) model that additionally includes AI techniques targeted at the key elements of user, usage, and usability. As a case study, we analyse a CTR prediction task, using deep learning methods (factorization machines) to predict online fraud through clickbait. The results of experiments on a real-world benchmark Avazu dataset show that the proposed approach outpaces (AUC is 0.8062) other CTR forecasting approaches, demonstrating the viability of the proposed framework.