Assoc. Prof. Mathew Nicho, University of Queensland, Australia
Dr. Mathew Nicho is an Associate Professor of Cybersecurity at Rabdan Academy, UAE, and an Adjunct Associate Professor at UQ Cyber, School of Electrical Engineering and Computer Science, University of Queensland, Australia. He teaches cybersecurity to defence and law enforcement personnel in the UAE while conducting government-commissioned research. He holds a Master’s (2004) and Ph.D. (2009) in IT from Auckland University of Technology (AUT), New Zealand, and has taught at universities in New Zealand, the UK, and the UAE, bringing a global perspective to his academic and research endeavors.
Dr. Nicho’s teaching excellence is recognized through the Fellowship of the Higher Education Academy (FHEA, 2016) and four digital learning certifications from Blackboard Academy UK (2020–2021). He was nominated as an ‘Exceptional’ faculty member in 2018 and received the ‘Exceptional Faculty’ award in 2019. His research portfolio includes 70+ publications (72% as first author), spanning Q1–Q4 journals and conferences, with continuous research grants since 2014.
His recent industry-academic collaborations include a technical report on cybersecurity in the UAE’s financial sector, conducted in partnership with ADGM Academy and the University of Queensland, as well as a collaboration with Abu Dhabi Police on crime prediction. As an industry speaker with multiple media interviews, Dr. Nicho has forged strong and ongoing connections with the IT security industry.
A dedicated academic, Dr. Nicho has supervised numerous Ph.D. and MSc theses, resulting in joint publications. His research interests lie at the intersection of cybersecurity, attacks on national critical infrastructure, machine learning, and situational cybercrime criminology, and digital learning, ensuring a strong alignment between academic theory and industry practice.
Speech Title: Dimensionality Reduction for Enhancing Malware Classification Accuracy in Portable Executable Files
Abtract: Portable executable (PE) files are a common vector used for the spread of malware. This paper reviews and evaluates machine learning-based PE malware detection techniques. A dataset was created using malicious samples from Virus Share and benign samples from github. Static analysis was used to extract highly ranked features and reduce the dimensions of the dataset using both Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA). K-Nearest Neighbors and Random Forest classifiers were shown to perform well returning accuracy between ≈93% and ≈94% when used in combination with Linear Discriminant Analysis (LDA).
Dr. Yang Liu, Department of Computer Science, Swansea University, UK
Dr. Yang Liu received his D.Phil. in Computer Science from University of Oxford in 2018. He is currently a Senior Lecturer in the Department of Computer Science at Swansea University. Before joining Swansea University, he served as an Assistant Professor in Harbin Institute of Technology (Shenzhen) from 2018 to 2024. His research interests focus on data security and privacy-preserving computing. He has expertise in federated learning, blockchain applications, and the design of privacy protection mechanisms. His current work explores the convergence of artificial intelligence and cybersecurity technologies, aiming to enhance the robustness and security of intelligent systems.
Speech Title: A Privacy-Preserving Mechanism for Targeted Mobile Advertising
Abtract: The prevalent model of the current Internet economy allows consumers to access free services in exchange for targeted advertising. This approach leverages personal data collected from users’ devices to deliver tailored advertisements, offering significant benefits to advertisers. However, targeted advertising raises several concerns, with privacy-related issues being particularly prominent. This talk introduces a simple privacy-preserving mechanism designed to address these concerns in Targeted Mobile Advertising while maintaining the current business model. By prioritizing consumers' privacy, the mechanism aims to build trust and enhance advertising outcomes. It also serves as a foundation to inspire innovative solutions for balancing privacy and personalization in mobile advertising.