As the demand for cybersecurity experts in the industry grows, we face a widening shortage of skilled professionals. This pressing concern has spurred extensive research within academia and national bodies, who are striving to bridge this skills gap through refined educational frameworks, including the integration of innovative information applications like remote laboratories and virtual classrooms. Despite these initiatives, current higher education models for cybersecurity, while effective in some areas, fail to provide a holistic solution to the root causes of the skills gap. Our study conducts a thorough examination of established cybersecurity educational frameworks, with the goal of identifying crucial learning outcomes that can mitigate the factors contributing to this skills gap. Furthermore, by analyzing six different educational models, for each one that can uniquely leverage technology like virtual classrooms and online platforms and is suited to various learning contexts, we categorize these contexts into four distinct categories. This categorization introduces a holistic dimension of context awareness enriched by digital learning tools into the process, enhancing the alignment with desired learning outcomes, a consideration sparsely addressed in the existing literature. This thorough analysis further strengthens the framework for guiding education providers in selecting models that most effectively align with their targeted learning outcomes and implies practical uses for technologically enhanced environments. This review presents a roadmap for educators and institutions, offering insights into relevant teaching models, including the opportunities for the utilization of remote laboratories and virtual classrooms, and their contextual applications, thereby aiding curriculum designers in making strategic decisions.

Despite the focus on improving cybersecurity awareness, the number of cyberattacks has increased significantly, leading to huge financial losses, with their risks spreading throughout the world. This is due to the techniques deployed in cyberattacks that mainly aim at exploiting humans, the weakest link in any defence system. The existing literature on human factors in phishing attacks is limited and does not live up to the witnessed advances in phishing attacks, which have become exponentially more dangerous with the introduction of generative artificial intelligence (GenAI). This paper studies the implications of AI advancement, specifically the exploitation of GenAI and human factors in phishing attacks. We conduct a systematic literature review to study different human factors exploited in phishing attacks, potential solutions and preventive measures, and the complexity introduced by GenAI-driven phishing attacks. This paper aims to address the gap in the research by providing a deeper understanding of the evolving landscape of phishing attacks with the application of GenAI and associated human implications, thereby contributing to the field of knowledge to defend against phishing attacks by creating secure digital interactions.

Due to the widespread applicability of generative artificial intelligence, we have seen it adopted across many areas of education, providing universities with new opportunities, particularly in cybersecurity education. With the industry facing a skills shortage, this paper explores the use of generative artificial intelligence in higher cybersecurity education as an intelligent tutoring system to enhance factors leading to positive student outcomes. Despite its success in content generation and assessment within cybersecurity, the field’s multidisciplinary nature presents additional challenges to scalability and generalisability. We propose a solution using agents to orchestrate specialised large language models and to demonstrate its applicability in graduate level cybersecurity topics offered at a leading Australian university. We aim to show a generalisable and scalable solution to diversified educational paradigms, highlighting its relevant features, and a method to evaluate the quality of content as well as the general effectiveness of the intelligent tutoring system on subjective factors aligned with positive student outcomes. We further explore areas for future research in model efficiency, privacy, security, and scalability.

This paper presents a comprehensive investigation into the role, functionalities, and complexities of blockchain oracles, focusing particularly on the implications for smart contracts in legal reasoning contexts. Oracles serve as a vital bridge to smart contracts’ inability to interact with external or “off-chain” data, enabling them to be used in a variety of real-world situations. Oracle’s integration, however, introduces a number of complexities, including security vulnerabilities, collectively referred to as the Oracle Problem. In addition to a review of existing literature, we also provide a mathematical analysis quantifying the computational complexity associated with automating legal reasoning and a novel design framework aimed at establishing oracles that are secure, efficient, and legally compliant. The paper aims to serve as a foundational text for researchers, legal practitioners, and blockchain developers, advancing the academic discourse surrounding blockchain oracles and their role in smart contracts.

Automatic Speech Recognition (ASR) systems are ubiquitous in various commercial applications. These systems typically rely on machine learning techniques for transcribing voice commands into text for further processing. Despite their success in many applications, audio Adversarial Examples (AEs) have emerged as a major security threat to ASR systems. This is because audio AEs are able to fool ASR models into producing incorrect results. While researchers have investigated methods for defending against audio AEs, the intrinsic properties of AEs and benign audio are not well studied. The work in this paper shows that the machine learning decision boundary patterns around audio AEs and benign audio are fundamentally different. Using dimensionality-reduction techniques, this work shows that these different patterns can be visually distinguished in two-dimensional (2D) space. This in turn allows for the detection of audio AEs using anomal- detection methods.