A successful application of an Internet of Things (IoT) based network depends on the accurate and successful delivery of data collected from numerous sources. A significant concern in IoT systems arises when end-users do not have sufficient transparency and are unaware of any potential data manipulation and risk in each step involved in data propagation. One potential solution is to integrate security metadata in IoT-based security-aware provenance graphs that provides better transparency with security awareness at each step of data propagation. In this paper, we integrate security metadata into the provenance graph with predefined security policies. We design a hypothetical IoT-Health scenario with possible threats: node cloning, fault packet injection, denial of service, unauthorized access, and malicious code injection. We simulate these threats in six cases to identify relevant risks. Our findings show how a security-aware provenance graph can offer end users greater transparency and security awareness by identifying failed signature verification (case 1), denial of service (case 2), unauthorized access (case 3), intrusion detection (case 4), missing WAF (case 5), and permission violation (case 6). We evaluate the transparency through obtaining authentication, integrity, availability and detecting underlying threats. Accordingly, this study promotes better risk assessment and decision-making for users with negligible performance overhead.

End-users in a decision-oriented Internet of Things (IoT) healthcare system are often left in the dark regarding critical security information necessary for making informed decisions about potential risks. This is partly due to the lack of transparency and system security awareness end-users have in such systems. To empower end-users and enhance their cybersecurity situational awareness, it is imperative to thoroughly document and report the runtime security controls in place, as well as the security-relevant aspects of the devices they rely on, while the need for better transparency is obvious, it remains uncertain whether current systems offer adequate security metadata for end-users and how future designs can be improved to ensure better visibility into the security measures implemented. To address this gap, we conducted table-top exercises with ten security and ICT experts to evaluate a typical IoT-Health scenario. These exercises revealed the critical role of security metadata, identified the available ones to be presented to users, and suggested potential enhancements that could be integrated into system design. We present our observations from the exercises, highlighting experts’ valuable suggestions, concerns, and views, backed by our in-depth analysis. Moreover, as a proof-of-concept of our study, we simulated three relevant use cases to detect cyber risks. This comprehensive analysis underscores critical considerations that can significantly improve future system protocols, ensuring end-users are better equipped to navigate and mitigate security risks effectively.

Exploring human trust in artificial intelligence (AI) in Law Enforcement domain is paramount for its ethical and effective deployment. As AI systems become increasingly integrated into society, trust ensures transparency, accountability, and fairness in their deployment. Despite the rapid increase in discussion about AI usage in law enforcement in various sectors globally, no secondary research explores that for OCEANIA, specifically in Australia. Moreover, how humans are considered in AI law enforcement is an uninvestigated area in this region. This paper aims to investigate this gap by systematically exploring various applications used in this region and analyzing how human perspectives are used, and to what extent. We conducted a systematic literature review (SLR) based on 56 qualitatively selected studies to (i) classify the types, frequency, and demography of published research; (ii) explore how AI is leveraged; (iii) identify how and to what extent humans are considered in development and applications of AI in law enforcement; (iv) understand the challenges and solutions reported in this domain and worldwide. In summary, our findings point to the need for solutions to address operational and ethical issues in integrating AI into law enforcement and for unified legislation or regulations to effectively design and utilize AI in this domain. Based on the findings that highlight the important concerns in this domain, we recommend a list of future research directions.

The growth of mobile financial services over the last decade is well accepted by the people of Bangladesh. However, although reported here and there, a complete understanding of illegal payments and fraudulent transactions is still missing. This paper studies one of the popular and experienced financial transaction services of Bangladesh, Bkash, to identify fraudulent text messages through the service. We propose an improved classification method for fraudulent messages through feature extraction and a hybrid deep learning (DL) approach with ML, which combines a convolutional neural network with sequential learning, referred to as the customized Bkash fraud semantic sequential learning (CBF-SSL) model. Seven machine-learning classifiers and five deep-learning sequential models are experimentally evaluated within the hybrid classifier. The performance of the fraud detection system is assessed using the loss function and confusion matrix on a dataset of 500 Bengali text messages. The performance evaluation demonstrates that the hybrid model of convolution neural network (CNN) using long short-term memory (LSTM) outperforms all other classifiers with the lowest error 1 rate of 0.2532%, the highest F1-measure of 75.00% with the highest test accuracy. The proposed text features in combination with CNN and LSTM prove to be highly effective in detecting fraudulent messages.

Exploring human trust in Artificial Intelligence (AI) in Law Enforcement domain is paramount for its ethical and effective deployment. As AI systems become increasingly integrated into society, trust ensures transparency, accountability, and fairness in their deployment. Despite the rapid increase in discussion about AI usage in law enforcement in various sectors globally, no secondary research explores that for OCEANIA, specifically in Australia. Moreover, how humans are considered in AI law enforcement is an uninvestigated area in this region. This paper aims to investigate this gap by systematically exploring various applications used in this region and analysing how human perspectives are used, and to what extent. We conducted a systematic literature review (SLR) based on 56 qualitatively selected studies to i) classify the types, frequency, and demography of published research; ii) explore how AI is leveraged; iii) identify how and to what extent humans are considered in development and applications of AI in law enforcement; iv) understand the challenges and solutions reported in this domain and worldwide. In summary, our findings point to the need for solutions to address operational and ethical issues in integrating AI into law enforcement and for unified legislation or regulations to effectively design and utilize AI in this domain. Based on the findings that highlight the important concerns in this domain, we recommend a list of future research directions.