Matthew Roughan

Ayyoob Hamza, Dinesha Ranathunga, H. Habibi Gharakheili et al.

IoT devices are increasingly being implicated in cyber-attacks, driving community concern about the risks they pose to critical infrastructure, corporations, and citizens. In order to reduce this risk, the IETF is pushing IoT vendors to develop formal specifications of the intended purpose of their IoT devices, in the form of a Manufacturer Usage Description (MUD), so that their network behavior in any operating environment can be locked down and verified rigorously. This paper aims to assist IoT manufacturers in developing and verifying MUD profiles, while also helping adopters of these devices to ensure they are compatible with their organizational policies. Our first contribution is to develop a tool that takes the traffic trace of an arbitrary IoT device as input and automatically generates a MUD profile for it. We contribute our tool as open source, apply it to 28 consumer IoT devices, and highlight insights and challenges encountered in the process. Our second contribution is to apply a formal semantic framework that not only validates a given MUD profile for consistency, but also checks its compatibility with a given organizational policy. Finally, we apply our framework to representative organizations and selected devices, to demonstrate how MUD can reduce the effort needed for IoT acceptance testing.

Kwong Ho Li, Matthew Roughan, Wathsala Karunarathne

Staff absenteeism imposes substantial operational costs in high-demand work environments such as healthcare, emergency services, meat processing, construction, and courier and delivery services, where proactive workforce planning depends on reliable individual-level absence prediction. Existing regression and classification approaches share a structural limitation; they map features observed at time t to labels at the same time t, reproducing already-realised outcomes rather than predicting future events, and discard the sequential behavioural structure inherent in individual attendance histories. We propose a Time Series Classification (TSC) framework that separates historical attendance sequences from future absence labels, enabling genuinely proactive prediction. Due to the lack of public longitudinal attendance data, we construct a reproducible simulated dataset calibrated to the UCI dataset. We analyse Binary Focal Loss (BFL) and Geometric Mean (G-Mean) loss under severe class imbalance using only the imbalance ratio $ρ$. For BFL, the initial gradient ratio is $ρα/(1-α)$, implying the balanced weight $α= 1/(1+ρ) \approx 0.023$. Experiments show that performance is governed mainly by $α$, with BFL achieving specificity 0.813 and balanced accuracy 0.888, comparable to G-Mean. Unlike BFL, G-Mean adapts automatically without parameter calibration. Among three deep learning architectures evaluated, Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and the hybrid LSTM-Fully Convolutional Network (LSTM-FCN), the LSTM-FCN delivers strong precision and specificity. Stable performance is obtained with batch sizes >= 64 and window sizes between 40-80 days, yielding balanced accuracy of approximately 80% on held-out test data.

Yi Ren, Lewis Mitchell, Matthew Roughan

Understanding moral values in social media text offers insight into moral judgement formation, and supervised NLP models trained on crowdsourced data have achieved strong classification performance. However, most approaches simplify the problem by aggregating multiple annotators' labels into a single "ground truth", overlooking the inherent subjectivity of the task. In practice, there are disagreements between annotators caused by personal viewpoint or inherent ambiguities, particularly for short tweets. Here, we extend a pretrained language model with a layer that learns annotator-specific features. Our model improves predictions of individual annotations and yields representations that reveal meaningful insights into annotators' moral perspectives. We show that models trained on aggregated labels may hide variation and give a misleading impression of performance. Overall, we demonstrate that disagreement reflects the inherent subjectivity of the task and that modelling individual perspectives creates benefits for moral classification of texts.

Benjamin Lang, Matthew Roughan, Mengbin Ye

Australia depends on an Internet built from multiple networks of long-haul links. We study the interactions of these independent provider networks to investigate how the peering between these networks provides redundancy for failures on a single network, as well as the potential vulnerabilities introduced by failures of Shared Risk Link Groups (SRLGs), whereby ostensibly independent links of different providers fail simultaneously due to joint physical dependencies such as shared conduits. We introduce a generalised multilayer network model in which each layer represents the network of an individual Internet Service Provider (ISP), along with an Internet Exchange Point (IXP) layer that facilitates interconnections between ISP networks. We construct an Australia-specific model, consisting of six major ISPs. A failure analysis is performed on this network, revealing that diversity provides redundancy, even in the presence of shared risks, indicating the importance of a diverse network ecosystem.

Samudra Herath, Matthew Roughan, Gary Glonek

Accurate and efficient entity resolution (ER) is a significant challenge in many data mining and analysis projects requiring integrating and processing massive data collections. It is becoming increasingly important in real-world applications to develop ER solutions that produce prompt responses for entity queries on large-scale databases. Some of these applications demand entity query matching against large-scale reference databases within a short time. We define this as the query matching problem in ER in this work. Indexing or blocking techniques reduce the search space and execution time in the ER process. However, approximate indexing techniques that scale to very large-scale datasets remain open to research. In this paper, we investigate the query matching problem in ER to propose an indexing method suitable for approximate and efficient query matching. We first use spatial mappings to embed records in a multidimensional Euclidean space that preserves the domain-specific similarity. Among the various mapping techniques, we choose multidimensional scaling. Then using a Kd-tree and the nearest neighbour search, the method returns a block of records that includes potential matches for a query. Our method can process queries against a large-scale dataset using only a fraction of the data $L$ (given the dataset size is $N$), with a $O(L^2)$ complexity where $L \ll N$. The experiments conducted on several datasets showed the effectiveness of the proposed method.

Samudra Herath, Matthew Roughan, Gary Glonek

The recent rapid growth of the dimension of many datasets means that many approaches to dimension reduction (DR) have gained significant attention. High-performance DR algorithms are required to make data analysis feasible for big and fast data sets. However, many traditional DR techniques are challenged by truly large data sets. In particular multidimensional scaling (MDS) does not scale well. MDS is a popular group of DR techniques because it can perform DR on data where the only input is a dissimilarity function. However, common approaches are at least quadratic in memory and computation and, hence, prohibitive for large-scale data. We propose an out-of-sample embedding (OSE) solution to extend the MDS algorithm for large-scale data utilising the embedding of only a subset of the given data. We present two OSE techniques: the first based on an optimisation approach and the second based on a neural network model. With a minor trade-off in the approximation, the out-of-sample techniques can process large-scale data with reasonable computation and memory requirements. While both methods perform well, the neural network model outperforms the optimisation approach of the OSE solution in terms of efficiency. OSE has the dual benefit that it allows fast DR on streaming datasets as well as static databases.

Samudra Herath, Matthew Roughan, Gary Glonek

Accurate and efficient entity resolution (ER) has been a problem in data analysis and data mining projects for decades. In our work, we are interested in developing ER methods to handle big data. Good public datasets are restricted in this area and usually small in size. Simulation is one technique for generating datasets for testing. Existing simulation tools have problems of complexity, scalability and limitations of resampling. We address these problems by introducing a better way of simulating testing data for big data ER. Our proposed simulation model is simple, inexpensive and fast. We focus on avoiding the detail-level simulation of records using a simple vector representation. In this paper, we will discuss how to simulate simple vectors that approximate the properties of names (commonly used as identification keys).

Dinesha Ranathunga, Matthew Roughan, Paul Tune et al.

Firewall configuration is critical, yet often conducted manually with inevitable errors, leaving networks vulnerable to cyber attack [40]. The impact of misconfigured firewalls can be catastrophic in Supervisory Control and Data Acquisition (SCADA) networks. These networks control the distributed assets of industrial systems such as power generation and water distribution systems. Automation can make designing firewall configurations less tedious and their deployment more reliable. In this paper, we propose ForestFirewalls, a high-level approach to configuring SCADA firewalls. Our goals are three-fold. We aim to: first, decouple implementation details from security policy design by abstracting the former; second, simplify policy design; and third, provide automated checks, pre and post-deployment, to guarantee configuration accuracy. We achieve these goals by automating the implementation of a policy to a network and by auto-validating each stage of the configuration process. We test our approach on a real SCADA network to demonstrate its effectiveness.

Dinesha Ranathunga, Matthew Roughan, Phil Kernick et al.

A common requirement in policy specification languages is the ability to map policies to the underlying network devices. Doing so, in a provably correct way, is important in a security policy context, so administrators can be confident of the level of protection provided by the policies for their networks. Existing policy languages allow policy composition but lack formal semantics to allocate policy to network devices. Our research tackles this from first principles: we ask how network policies can be described at a high-level, independent of firewall-vendor and network minutiae. We identify the algebraic requirements of the policy mapping process and propose semantic foundations to formally verify if a policy is implemented by the correct set of policy-arbiters. We show the value of our proposed algebras in maintaining concise network-device configurations by applying them to real-world networks.

Matthew Roughan, Jonathan Tuke

A graph is used to represent data in which the relationships between the objects in the data are at least as important as the objects themselves. Over the last two decades nearly a hundred file formats have been proposed or used to provide portable access to such data. This paper seeks to review these formats, and provide some insight to both reduce the ongoing creation of unnecessary formats, and guide the development of new formats where needed.