Smart environments are increasingly equipped with interconnected digital systems to manage access and physical security. However, traditional authentication methods, typically restricted to static checkpoints, fail to provide persistent assurance once entry is granted, leaving facilities vulnerable to credential misuse, tailgating, and unauthorised movement. This paper presents the Continuous Authentication Platform (CAP), a modular, multi-modal framework developed within the RAAISE project to enable continuous and context-aware verification across dynamic facility zones. CAP integrates heterogeneous off-the-shelf sensors, including NFC, RFID, biometric, motion, and WiFi positioning units, which collectively support persistent user tracking and real-time access enforcement. The platform’s architecture couples distributed sensing and edge processing with a centralised intelligence layer for event correlation and policy-driven decision-making. A live testbed deployment at Deakin University was used to evaluate CAP’s performance under realistic operational conditions. Results from functional trials demonstrate CAP’s ability to detect credential misuse, prevent tailgating, and maintain authentication continuity with sub-second responsiveness. These findings underscore CAP’s potential as a scalable, privacy-aligned foundation for next-generation smart facility security systems.

The concept of Verifiable Credentials (VC) has emerged as a viable alternative to federated identity systems and can offer greater levels of control and ownership to users over their Digital Identity. However, the inability of users to make optimal decisions in relation to the use of VC results in privacy risks. To address this gap in VC technology, we present game-theoretic models for optimising the privacy of users and simultaneously ensuring minimum disclosure of PII in line with privacy safeguards around CDR and GDPR expectations around anonymity and unlinkability and demonstrate these properties through a digital credential wallet (DCW). The developed technology will deliver a novel DCW which embeds decision-making ability to quantify, benchmark and recommend the optimal usage of credentials that are held within the DCW.

Despite the enhanced security benefits offered by Physical Authentication Devices (PADs) compared to other forms of Multi-Factor Authentication (MFA), the adoption and retention of PADs remain relatively low in comparison to other MFA methods. Evidence indicates that the limited widespread adoption and usage of PADs are primarily due to negative user perceptions concerning their usability and security features. Moreover, there's a limited understanding of how users from diverse backgrounds perceive PADs with their varying standards and features. To bridge this knowledge gap, we undertook a multiple case study with 23 users spanning varied demographic characteristics (age, gender, education, and experience with MFA) to use and test three distinct PADs. Case study participants were provided with three unique PAD devices featuring different characteristics/features and were prompted to share their experiences of installation, usage, and troubleshooting over a 2-week span via an initial questionnaire, logbook, and a final interview. The gathered data were analysed using NVIVO, a Qualitative Research Software platform, uncovering notable disparities between user groups and their predilections for specific PADs. Further discussions from our research illuminate four primary areas (Compatibility, Support, Quality and Simplicity) where usable security features impede positive user perception of PAD devices and addressing these areas is crucial for enhancing PAD adoption and retention rates.

Physical authentication devices (PADs) offer a higher level of security than other authentication technologies commonly used in multifactor authentication (MFA) schemes because they are much less vulnerable to attack. However, PAD uptake remains significantly lower than that for SMS and app-based approaches, accounting for only 10% of all authentication technologies currently being utilized in MFA. Prior studies indicate that the primary reason for this low adoption rate is due to negative users' perceptions and attitudes toward the usability of PADs; many of these studies often skew toward a particular set of users (e.g., young university students, etc.), often creating a bias toward what usable security entails. To address this limitation, we have formulated an original research methodology that segments users into specific groups based on their user characteristics (i.e., age, education, and experience) and examines how each group defines usability and ranks their preferences regarding certain security features. Based on a survey of 410 participants, our results indicate that there are indeed different usable security preferences for each user group, and we, therefore, provide recommendations on how existing PADs might be enhanced to support usability and improve adoption rates.

The evolution of quantum computers poses a serious threat to contemporary public-key encryption (PKE) schemes. To address this impending issue, the National Institute of Standards and Technology (NIST) is currently undertaking the Post-Quantum Cryptography (PQC) standardization project intending to evaluate and subsequently standardize the suitable PQC scheme(s). One such attractive approach, called Bit Flipping Key Encapsulation (BIKE), has entered the final round of the competition. Despite having some attractive features, the IND-CCA security of BIKE depends on the average decoder failure rate (DFR), a higher value of which can facilitate a particular type of side-channel attack. Although BIKE adopts the Black-Grey-Flip (BGF) decoder that offers a negligible DFR, the effect of weak-keys on the average DFR has not been fully investigated. In this paper, we implement the BIKE scheme, and then through extensive experiments show that the weak-keys can be a potential threat to IND-CCA security of the BIKE scheme and thus need attention from the relevant research community. We also propose a key-check algorithm that can potentially supplement the BIKE mechanism and prevent users from adopting weak-keys.

A major concern in distributed networks is the ability to provide acceptable levels of security. This is achieved by using encryption and authentication mechanisms that depend on encryption keys. However, given the ever-expanding nature of the network, it is difficult to keep setting up authorities that can aid the key- exchange process. This paper presents a novel solution to the challenge of exchanging keys of a large, distributed network without the need to set up additional authorities. The key-exchange scheme presented takes advantage of features such as packet anonymity, random selection and a multi- path approach for the exchange process. The paper also discusses the effectiveness of the proposed scheme against various threat scenarios.

Edge computing is an emerging research area to incorporate cloud computing into edge network devices. An Edge datacenter, also referred to as EDC, processes data streams and user requests in real-time and is therefore used to decrease the latency and congestion in the network. EDC is usually setup as a distributed system and is accordingly placed between the cloud datacenter and the data source . These EDCs work as an intermediate layer in the fog hierarchy between IoT and Cloud datacenter. EDC’s are aided by load balancers, responsible for distributing the workload amongst multiple EDC, in order to optimize resource utilization and response time . The load balancers make sure that the workload is equally divided amongst the available EDCs to avoid over loading of some EDCs while other remain idle as this directly impacts the user response and real-time event detection . Given the fact that EDCs are deployed in remote environments, the need for secure authentication is of major importance. In this paper we propose a novel load balancing technique that enables EDC authentication as well as identification of idle EDCs for better load balancing. The proposed load balancing technique is also compared with existing approaches and proves to be more efficient in locating EDC’s with less workload. In addition to the improved efficiency, the proposed scheme also strengthens the security of the network by incorporating destination EDC authentication.

The World Wide Web serves as a huge repository of information that is highly dynamic, diverse and growing at an exponential rate in a lightening speed. In order to speed-up and further improve tasks like information search and retrieval, personalization etc; it is highly important to develop techniques to classify text documents more accurately and efficiently than before. This paper is an effort in that direction, where the effectiveness of Extreme Learning Machines(ELM) in the domain of text classification is studied and compared with many of the existing relevant techniques like Support Vector Machines(SVM), which are currently one of the most popular and effective techniques for classifying text documents. Ours is one of the few works that highlight the high performance of ELM in the field of text classification, by implementing classifiers based on different interpretations of ELM, analyzing their performance, and studying which feature selection techniques are most suited to improve their accuracy. In our multi-class classification problem, we studied a single ELM classifier based on the one-against-all scheme, and a multi-layer ELM classifier inspired from deep networks, and then perform extensive experiments on different datasets to demonstrate the applicability and effectiveness of our approach. Results show that ELM based classifiers can outperform many of the traditional classification techniques including the most powerful state-of-the-art technique such as SVM.