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Purpose Supply chain risks (SCRs) do not work in isolation and have impact both on each member of a chain and the performance of the entire supply chain. The purpose of this paper is to quantitatively assess the impact of dynamic risk propagation within and between integrated firms in global fresh produce supply chains. Design/methodology/approach A risk propagation ontology-based Bayesian network (BN) model was developed to measure dynamic SCR propagation. The proposed model was applied to a two-tier Australia-China table grape supply chain (ACTGSC) featured with an upstream Australian integrated grower and exporter and a downstream Chinese integrated importer and online retailer. Findings An ontology-based BN can be generated to accurately represent the risk domain of interest using the knowledge and inference capabilities inherent in a risk propagation ontology. In addition, the analyses revealed that supply discontinuity, product inconsistency and/or delivery delay originating in the upstream firm can propagate to increase the downstream firm’s customer value risk and business performance risk. Research limitations/implications The work was conducted in an Australian-China table grape supply chain, so results are only product chain-specific in nature. Additionally, only two state values were considered for all nodes in the model, and finally, while the proposed methodology does provide a large-scale risk network map, it may not be appropriate for a large supply chain network as it only follows the process flow of a single supply chain. Practical implications This study supports the backward-looking traceability of risk root causes through the ACTGSC and the forward-looking prediction of risk propagation to key risk performance measures. Social implications The methodology used in this paper provides an evidence-based decision-making capability as part of a system-wide risk management approach and fosters collaborative SCR management, which can yield numerous societal benefits. Originality/value The proposed methodology addresses the challenges in using a knowledge-based approach to develop a BN model, particularly with a large-scale model and integrates risk and performance for a holistic risk propagation assessment. The combination of modelling approaches to address the issue is unique.

The BeefLedger Export Smart Contracts project is a collaborative research study between BeefLedger Ltd and QUT co-funded by the Food Agility CRC. This project exists to deliver economic value to those involved in the production, export and consumption of Australian beef to China through: (1) reduced information asymmetry; (2) streamlined compliance processes, and; (3) developing and accessing new data-driven value drivers, through the deployment of decentralised ledger technologies and associated governance systems. This report presents early insights from a survey deployed to Chinese consumers in Nov/Dec 2019 exploring attitudes and preferences about blockchain-credentialed beef exports to China. Our results show that most local and foreign consumers were willing to pay more than the reference price for a BeefLedger branded Australian cut and packed Sirloin steak at the same weight. Although considered superior over Chinese processed Australian beef products, the Chinese market were sceptical that the beef they buy was really from Australia, expressing low trust in Australian label and traceability information. Despite lower trust, most survey respondents were willing to pay more for traceability supported Australian beef, potentially because including this information provided an additional sense of safety. Therefore, traceability information should be provided to consumers, as it can add a competitive advantage over products without traceability.

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Application-layer distributed denial of service (AL-DDoS) attacks are becoming critical threats to websites because the stealth of AL-DDoS attacks makes many intrusion prevention systems ineffective. To detect AL-DDoS attacks aimed at websites, we propose a novel statistical model called the RM (rhythm matrix). Although the original features from the network layer are adopted, the access trajectory, including requested objects and corresponding dwell-time values, can be abstracted and accumulated into an RM. With an RM, we can almost losslessly compress complex features into a simple structure and characterize the user access behavior. We detect AL-DDoS attacks according to the increase of the abnormality degree in the RM and further identify malicious hosts based on change-rate outliers. In the experiments, we simulate three modes of AL-DDoS attacks with the latest popular DDoS attack tools: LOIC and HOIC. The results show that our method can detect these simulated attacks and identify the malicious hosts accurately and efficiently. For an AL-DDoS detection method, the ability to distinguish flash crowds is indispensable. We also demonstrate the excellent performance of our approach in distinguishing flash crowds from AL-DDoS attacks with two reconstructed public datasets.