Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks

Federated Learning (FL) has recently become an effective approach for cyberattack detection systems, especially in Internet-of-Things (IoT) networks. By distributing the learning process across IoT gateways, FL can improve learning efficiency, reduce communication overheads and enhance privacy for cyberattack detection systems. Challenges in implementation of FL in such systems include unavailability of labeled data and dissimilarity of data features in different IoT networks. In this paper, we propose a novel collaborative learning framework that leverages Transfer Learning (TL) to overcome these challenges. Particularly, we develop a novel collaborative learning approach that enables a target network with unlabeled data to effectively and quickly learn knowledge from a source network that possesses abundant labeled data. It is important that the state-of-the-art studies require the participated datasets of networks to have the same features, thus limiting the efficiency, flexibility as well as scalability of intrusion detection systems. However, our proposed framework can address these problems by exchanging the learning knowledge among various deep learning models, even when their datasets have different features. Extensive experiments on recent real-world cybersecurity datasets show that the proposed framework can improve more than 40% as compared to the state-of-the-art deep learning based approaches.Comment: 12 page

Young Australians, illness and education: report on the national database project

Chronically-ill children who need to take extended absences from school are neither officially acknowledged nor assisted to keep up, according to this report. Executive summary Australia’s health system faces many challenges related to chronic and complex health conditions. Major advances in biomedicine mean that children and young people are now surviving conditions that would have meant early death just a short time ago. This has major implications for Australia’s education systems.   A troubling side effect of this success in medicine is the number of children and young people who manage their chronic health conditions, but who can easily remain overlooked in education. In many ways this is a new frontier for teachers, schools and education systems. Never before have so many students been present in our classrooms, who have survived major health challenges, but who are not yet systematically supported to thrive in education.   This report provides a detailed summary of education, health and demographic information about Australian children and young people who live with significant health conditions. This study closely examined an important national database of 2360 such individuals.   This study is contextualised within recent international literature from the fields of education, medicine, oncology, public health, paediatric nursing, psychology, counselling, psychiatry and social policy. Socio-economic indexes developed by the Australian Bureau of Statistics were employed in the analysis of data.   As Australian government figures are not available about these young people as an educational cohort, the major purpose of this study was to provide an empirical base for policy recommendation and further investigation. An important secondary aim of the study was to contribute to the growing body of evidence about pressing educational issues related to these children and young people. Concern about the education of this group is expected to escalate as this cohort of Australian students continues to expand, due to advances in biomedicine

Multi-graph-view subgraph mining for graph classification

© 2015, Springer-Verlag London. In this paper, we formulate a new multi-graph-view learning task, where each object to be classified contains graphs from multiple graph-views. This problem setting is essentially different from traditional single-graph-view graph classification, where graphs are collected from one single-feature view. To solve the problem, we propose a cross graph-view subgraph feature-based learning algorithm that explores an optimal set of subgraphs, across multiple graph-views, as features to represent graphs. Specifically, we derive an evaluation criterion to estimate the discriminative power and redundancy of subgraph features across all views, with a branch-and-bound algorithm being proposed to prune subgraph search space. Because graph-views may complement each other and play different roles in a learning task, we assign each view with a weight value indicating its importance to the learning task and further use an optimization process to find optimal weight values for each graph-view. The iteration between cross graph-view subgraph scoring and graph-view weight updating forms a closed loop to find optimal subgraphs to represent graphs for multi-graph-view learning. Experiments and comparisons on real-world tasks demonstrate the algorithm’s superior performance

Learning Informative Representation for Fairness-aware Multivariate Time-series Forecasting: A Group-based Perspective

Performance unfairness among variables widely exists in multivariate time series (MTS) forecasting models since such models may attend/bias to certain (advantaged) variables. Addressing this unfairness problem is important for equally attending to all variables and avoiding vulnerable model biases/risks. However, fair MTS forecasting is challenging and has been less studied in the literature. To bridge such significant gap, we formulate the fairness modeling problem as learning informative representations attending to both advantaged and disadvantaged variables. Accordingly, we propose a novel framework, named FairFor, for fairness-aware MTS forecasting. FairFor is based on adversarial learning to generate both group-independent and group-relevant representations for the downstream forecasting. The framework first leverages a spectral relaxation of the K-means objective to infer variable correlations and thus to group variables. Then, it utilizes a filtering&fusion component to filter the group-relevant information and generate group-independent representations via orthogonality regularization. The group-independent and group-relevant representations form highly informative representations, facilitating to sharing knowledge from advantaged variables to disadvantaged variables to guarantee fairness. Extensive experiments on four public datasets demonstrate the effectiveness of our proposed FairFor for fair forecasting and significant performance improvement.Comment: 13 pages, 5 figures, accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE

Use of the self-organising map network (SOMNet) as a decision support system for regional mental health planning

Background: Decision-making in mental health systems should be supported by the evidence-informed knowledge transfer of data. Since mental health systems are inherently complex, involving interactions between its structures, processes and outcomes, decision support systems (DSS) need to be developed using advanced computational methods and visual tools to allow full system analysis, whilst incorporating domain experts in the analysis process. In this study, we use a DSS model developed for interactive data mining and domain expert collaboration in the analysis of complex mental health systems to improve system knowledge and evidence-informed policy planning. Methods: We combine an interactive visual data mining approach, the self-organising map network (SOMNet), with an operational expert knowledge approach, expert-based collaborative analysis (EbCA), to develop a DSS model. The SOMNet was applied to the analysis of healthcare patterns and indicators of three different regional mental health systems in Spain, comprising 106 small catchment areas and providing healthcare for over 9 million inhabitants. Based on the EbCA, the domain experts in the development team guided and evaluated the analytical processes and results. Another group of 13 domain experts in mental health systems planning and research evaluated the model based on the analytical information of the SOMNet approach for processing information and discovering knowledge in a real-world context. Through the evaluation, the domain experts assessed the feasibility and technology readiness level (TRL) of the DSS model. Results: The SOMNet, combined with the EbCA, effectively processed evidence-based information when analysing system outliers, explaining global and local patterns, and refining key performance indicators with their analytical interpretations. The evaluation results showed that the DSS model was feasible by the domain experts and reached level 7 of the TRL (system prototype demonstration in operational environment). Conclusions: This study supports the benefits of combining health systems engineering (SOMNet) and expert knowledge (EbCA) to analyse the complexity of health systems research. The use of the SOMNet approach contributes to the demonstration of DSS for mental health planning in practice