• The paper identifies the current trends in key security technologies for IoT-based smart farming through a systematic mapping study. • The paper assesses the technology readiness level (TRL) of each identified key security solution and the characteristics of a security product identified by ISO/IEC 25010. • Mapping of ISO/IEC 25010 security characteristics and Technology Readiness Level Smart farming systems sit at the intersection between three rapidly and independently advancing fields of IoT, Security, and Machine Learning. Its full realisation has tremendous positive impacts on food production; yet agricultural settings come with unique challenges that inhibit the rapid deployment of such state-of-the-art technologies. In this paper, we systematically study the current state of security for IoT-based smart farming research and development landscape and assess the proposed security solutions through the lens of technology readiness levels (TRL) and ISO/IEC 25010 security product evaluation framework. By analysing forty-eight primary studies, we identified the top security technologies under development, the critical security threats being addressed, and the most popularly used machine learning-based security solutions. Furthermore, we found that most of the ISO/IEC 25010 security characteristics considered by the security solutions are currently below TRL 6, indicating that they are well below the deployment readiness levels. Therefore, we recommend several supporting transitional technologies be developed to move the prototype development towards system validation and deployment to avoid the technology “valley of death”, such as farming-specific intrusion detection public datasets and large-scale IoT agriculture testbeds to validate the interoperability and transparency of security solutions at different layers. This systematic mapping study, together with a TRL assessment and ISO 25010 standard mapping, is the first of its kind, intending to provide a standardised comparison of the current state of security technologies for IoT-based smart farms to define a clear roadmap for future research and development. It provides a common terminology for the multidisciplinary stakeholders of smart farming to distinguish between theoretical security concepts and ready-to-deploy solutions, facilitating crucial decisions for investment, deployment, and commercialisation.

Due to perceptions of efficiency and significant productivity gains, various organisations, including in education, are adopting Large Language Models (LLMs) into their workflows. Educator-facing, learner-facing, and institution-facing LLMs, collectively, Educational Large Language Models (eLLMs), complement and enhance the effectiveness of teaching, learning, and academic operations. However, their integration into an educational setting raises significant cybersecurity concerns. A comprehensive landscape of contemporary attacks on LLMs and their impact on the educational environment is missing. This study presents a generalised taxonomy of fifty attacks on LLMs, which are categorized as attacks targeting either models or their infrastructure. The severity of these attacks is evaluated in the educational sector using the DREAD risk assessment framework. Our risk assessment indicates that token smuggling, adversarial prompts, direct injection, and multi-step jailbreak are critical attacks on eLLMs. The proposed taxonomy, its application in the educational environment, and our risk assessment will help academic and industrial practitioners to build resilient solutions that protect learners and institutions.

Industrial Cyber-Physical Systems (ICPS) are heterogeneous computer systems interacting with physical processes in an industrial environment. The presence of numerous interconnected components poses significant security threats to ICPS. Slow-Rate Attacks (SRA), in which attackers attack a system constantly at low volumes, are difficult to detect for resource-constrained ICPS computers like programmable logic controllers (PLC). We propose an optimised light-weight active security framework for SRA detection based on Online Sequential Extreme Learning Machine (OSELM). We optimise the memory and space footprint of OSELM for deployment in resource-constrained ICPS. Additionally, a simple stratified k-fold cross training method improves the performance and accuracy of binary and multi-class SRA detection. Compared to existing methods, our technique requires less space and reduces attack detection time by at least 95%.

The significant growth in modern communication technologies has led to an increase in zero-day vulnerabilities that degrade the performance ofindustrialcyber-physical systems (ICPS). Distributed denial of service (DDoS) attacks are one such threat that overwhelms a target with floods of packets, posing a severe risk to the normal operations of the ICPS. Current solutions to detect DDoS attacks are unsuitable for resource-constrained ICPS. This study proposes actively detecting multiscale flooding DDoS attacks in resource-constrained ICPS by analyzing network traffic in the frequency domain. A two-phased technique detects attack presence and attack volume. Both phases use a novel combination of light-weight and theoretically sound statistical methods. The effectiveness of the proposed technique is evaluated using mainstream metrics like true and false positive rates, accuracy, and precision using BOUN DDoS 2020 and CICDDoS 2019 datasets. An implementation of the proposed approach on a programmable logic controllers-based ICPS demonstrated improvements in resource usage and detection time compared to the existing state-of-the-art.

Denial of Service is a significant availability threat in Industrial Cyber-Physical systems and smart manufacturing is not an exception. The types, methods, and duration of these attacks have been evolving rapidly and their number has increased dramatically, reaching a new record in history. In particular, digitisation of the manufacturing process and increased connectivity have created a battleground between product quality of service and threats associated with cross-domains and multi-vector attacks that affect the manufacturing system performance. The existing research on cyber-threats related to smart manufacturing system does not consider the comprehensive landscape of denial of service attacks. In this study, we classify well-accepted (distributed) denial of service attacks according to a proposed taxonomy, focusing on both the multi-vector attacks and cross-domain attacks. Utilising the taxonomy, more than fifty different denial of service attacks on smart manufacturing system were classified in terms of Endpoint and Network (distributed) denial of service attacks. As an example, a Cyber-Physical Conveyor System was used to examine the proposed taxonomy.

In Industrial Cyber-Physical Systems (ICPS), containerisation promises high scalability, reconfigurability and dependability. Denial of Service (DoD/DDoS) is a significant security threat in containerised ICPS applications, which execute on resource-constrained computers like PLCs, and cannot support traditional security mechanisms like firewalls that sacrifice performance and throughput. We propose a novel, light-weight active security approach to detecting DoS/DDoS attacks through frequency analysis of network traffic (packets). Our approach identifies attacks by recording a frequency signature of the flow of packets in an ICPS under normal operation. Subsequently, an attack is modelled as any anomalies in the network that modify the frequency profile of network traffic in the ICPS. Our prototype implementation and evaluation show that this active security method is light-weight and suitable for resource-constrained ICPS platforms.