School of Engineering, Computer and Mathematical Sciences - Te Kura Mātai Pūhanga, Rorohiko, Pāngarau

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https://hdl.handle.net/10292/553
AUT is home to a number of renowned research institutes in engineering, and computer and mathematical sciences. The School of Engineering, Computer and Mathematical Sciences strong industry partnerships and the unique combination of engineering, computer and mathematical sciences within one school stimulates interdisciplinary research beyond traditional boundaries. Current research interests include:
  • Artificial Intelligence; Astronomy and Space Research;
  • Biomedical Technologies;
  • Computer Engineering; Computer Vision; Construction Management;
  • Data Science;
  • Health Informatics and eHealth;
  • Industrial Optimisation, Modelling & Control;
  • Information Security;
  • Mathematical Sciences Research; Materials & Manufacturing Technologies;
  • Networking, Instrumentation and Telecommunications;
  • Parallel and Distributed Systems; Power and Energy Engineering;
  • Software Engineering; Signal Processing; STEM Education;
  • Wireless Engineering;

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Recent Submissions

Now showing 1 - 5 of 1431
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    Low-Voltage Solid State DCCB Design Based on Bypassed Bidirectional Thyristor-Capacitor Suppressor
    (Institute of Electrical and Electronics Engineers (IEEE), 2024-09-18) Moradian, M; Peykarporsan, R; Lie, TT; Gunawardane, K
    This article introduces a novel technique known as Bidirectional Thyristor Capacitor (BiTriCap) designed to interrupt DC currents effectively and mitigate power surges in low-voltage (LV) solid-state DC circuit breakers (SS-DCCB). The method employs parallel snubber capacitors to absorb switching effects, subsequently releasing stored energy during the subsequent switch operation. The model incorporates considerations for both line and load inductances, offering a realistic portrayal of a DC system and ensuring authentic protective measures. To validate the efficacy of this approach, practical results from the system are cross-referenced with simulation outputs, validating the credibility of the research findings. Additionally, an ARM microcontroller is programmed to control the sequence of actions among the active SS switches, optimizing their performance. The proposed LV SS-DCCB operates at a voltage level of 48 VDC and nominal current of 8 A. However, the design is scalable and can be extended to accommodate higher voltage and current ranges.
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    Evaluating the Cost of Classifier Discrimination Choices for IoT Sensor Attack Detection
    (Informa UK Limited, 2024-09-10) Nicho, M; Cusack, B; Girija, S; Arachchilage, N
    The intrusion detection of IoT devices through the classification of malicious traffic packets have become more complex and resource intensive as algorithm design and the scope of the problems have changed. In this research, we compare the cost of a traditional supervised pattern recognition algorithm (k-Nearest Neighbor (KNN)), with the cost of a current deep learning (DL) unsupervised algorithm (Convolutional Neural Network (CNN)) in their simplest forms. The classifier costs are calculated based on the attributes of design, computation, scope, training, use, and retirement. We find that the DL algorithm is applicable to a wider range of problem-solving tasks, but it costs more to implement and operate than a traditional classifier. This research proposes an economic classifier model for deploying suitable AI-based intrusion detection classifiers in IoT environments. The model was empirically validated on the IoT-23 dataset using KNN and CNN. This study closes a gap in prior research that mostly concentrated on technical elements by incorporating economic factors into the evaluation of AI algorithms for IoT intrusion detection. This research thus evaluated the economic implications of deploying AI-based intrusion detection systems in IoT environments, considering performance metrics, implementation costs, and the cost of classifier discrimination choices. Researchers and practitioners should focus on the cost–benefit trade-offs of any artificial intelligence application for intrusion detection, recommending an economic evaluation and task fit assessment before adopting automated solutions or classifiers for IoT intrusion detection, particularly in large-scale industrial settings that involve active attacks.
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    Reducing Instrumental Errors in Parkes Pulsar Timing Array Data
    (American Astronomical Society, 2024-09-23) Rogers, Axl F; van Straten, Willem; Gulyaev, Sergei; Parthasarathy, Aditya; Hobbs, George; Chen, Zu-Cheng; Feng, Yi; Goncharov, Boris; Kapur, Agastya; Liu, Xiaojin; Reardon, Daniel; Russell, Christopher J; Zic, Andrew
    This paper demonstrates the impact of state-of-the-art instrumental calibration techniques on the precision of arrival times obtained from 9.6 yr of observations of millisecond pulsars using the Murriyang 64 m CSIRO Parkes Radio Telescope. Our study focuses on 21 cm observations of 25 high-priority pulsars that are regularly observed as part of the Parkes Pulsar Timing Array project, including those predicted to be the most susceptible to calibration errors. We employ measurement equation template matching (METM) for instrumental calibration and matrix template matching (MTM) for arrival time estimation, resulting in significantly improved timing residuals with up to a sixfold reduction in white noise compared to arrival times estimated using scalar template matching and conventional calibration based on the ideal feed assumption. The median relative reduction in white noise is 33%, and the maximum absolute reduction is 4.5 μs. For PSR J0437−4715, METM and MTM reduce the best-fit power-law amplitude (2.7σ) and spectral index (1.7σ) of the red noise in the arrival time residuals, which can be tentatively interpreted as mitigation of 1/f noise due to otherwise unmodeled steps in polarimetric response. These findings demonstrate the potential to directly enhance the sensitivity of pulsar timing array experiments through more accurate methods of instrumental calibration and arrival time estimation.
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    A Comprehensive Review of Hybrid State Estimation in Power Systems: Challenges, Opportunities, and Prospects
    (MDPI AG, 2024-09-25) Kamyabi, Leila; Lie, Tek Tjing; Madanian, Samaneh; Marshall, Sarah
    Due to the increasing demand for electricity, competitive electricity markets, and economic concerns, power systems are operating near their stability margins. As a result, power systems become more vulnerable following disturbances, particularly from a dynamic point of view. To maintain the stability of power systems, operators need to continuously monitor and analyze the grid’s state. Since modern power systems are large-scale, non-linear, complex, and interconnected, it is quite challenging and computationally demanding to monitor, control, and analyze them in real time. State Estimation (SE) is one of the most effective tools available to assist operators in monitoring power systems. To enhance measurement redundancy in power systems, employing multiple measurement sources is essential for optimal monitoring. In this regard, this paper, following a brief explanation of the SE concept and its different categories, highlights the significance of Hybrid State Estimation (HSE) techniques, which combine the most used data resources in power systems, traditional Supervisory Control and Data Acquisition (SCADA) system measurements and Phasor Measurement Units (PMUs) measurements. Additionally, recommendations for future research are provided.
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    A Systematic Review of Deep Learning Techniques for Phishing Email Detection
    (MDPI AG, 2024-09-27) Kyaw, Phyo Htet; Gutierrez, Jairo; Ghobakhlou, Akbar
    The landscape of phishing email threats is continually evolving nowadays, making it challenging to combat effectively with traditional methods even with carrier-grade spam filters. Traditional detection mechanisms such as blacklisting, whitelisting, signature-based, and rule-based techniques could not effectively prevent phishing, spear-phishing, and zero-day attacks, as cybercriminals are using sophisticated techniques and trusted email service providers. Consequently, many researchers have recently concentrated on leveraging machine learning (ML) and deep learning (DL) approaches to enhance phishing email detection capabilities with better accuracy. To gain insights into the development of deep learning algorithms in the current research on phishing prevention, this study conducts a systematic literature review (SLR) following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. By synthesizing the 33 selected papers using the SLR approach, this study presents a taxonomy of DL-based phishing detection methods, analyzing their effectiveness, limitations, and future research directions to address current challenges. The study reveals that the adaptability of detection models to new behaviors of phishing emails is the major improvement area. This study aims to add details about deep learning used for security to the body of knowledge, and it discusses future research in phishing detection systems.
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