318 citations · 345 across the 10 of their papers we have counts for
11 papers
Forecasting Conceptual Diffusion in Science: The Case of Quantum Computing
Thomas Maillart, Thibaut Chataing, David Dosu +3
Understanding and anticipating scientific change requires models that distinguish between endogenous consolidation and exogenous diffusion of scientific concepts. Using the quantum…
Explainable Forecasting of Scientific Breakthroughs from Concept Network Dynamics
Thomas Maillart, Thibaut Chataing, Ntorina Antoni +4
We introduce an explainable machine-learning approach that forecasts the structural precursors of scientific breakthroughs -- the emergence and intensification of links between res…
Monitoring Transformative Technological Convergence Through LLM-Extracted Semantic Entity Triple Graphs
Alexander Sternfeld, Andrei Kucharavy, Dimitri Percia David +3
Forecasting transformative technologies remains a critical but challenging task, particularly in fast-evolving domains such as Information and Communication Technologies (ICTs). Tr…
Measuring Technological Convergence in Encryption Technologies with Proximity Indices: A Text Mining and Bibliometric Analysis using OpenAlex
Alessandro Tavazzi, Dimitri Percia David, Julian Jang-Jaccard +1
Identifying technological convergence among emerging technologies in cybersecurity is crucial for advancing science and fostering innovation. Unlike previous studies focusing on th…
Generative Adversarial Networks for Malware Detection: a Survey
Aeryn Dunmore, Julian Jang-Jaccard, Fariza Sabrina +1
Since their proposal in the 2014 paper by Ian Goodfellow, there has been an explosion of research into the area of Generative Adversarial Networks. While they have been utilised in…
Improving Multilayer-Perceptron(MLP)-based Network Anomaly Detection with Birch Clustering on CICIDS-2017 Dataset
Yuhua Yin, Julian Jang-Jaccard, Fariza Sabrina +1
Machine learning algorithms have been widely used in intrusion detection systems, including Multi-layer Perceptron (MLP). In this study, we proposed a two-stage model that combines…
Malware Triage Approach using a Task Memory based on Meta-Transfer Learning Framework
Jinting Zhu, Julian Jang-Jaccard, Ian Welch +2
To enhance the efficiency of incident response triage operations, it is not cost-effective to defend all systems equally in a complex cyber environment. Instead, prioritizing the d…
LSTM-Autoencoder based Anomaly Detection for Indoor Air Quality Time Series Data
Yuanyuan Wei, Julian Jang-Jaccard, Wen Xu +3
Anomaly detection for indoor air quality (IAQ) data has become an important area of research as the quality of air is closely related to human health and well-being. However, tradi…
IGRF-RFE: A Hybrid Feature Selection Method for MLP-based Network Intrusion Detection on UNSW-NB15 Dataset
Yuhua Yin, Julian Jang-Jaccard, Wen Xu +4
The effectiveness of machine learning models is significantly affected by the size of the dataset and the quality of features as redundant and irrelevant features can radically deg…
Evaluating the Security and Economic Effects of Moving Target Defense Techniques on the Cloud
Hooman Alavizadeh, Samin Aref, Dong Seong Kim +1
Moving Target Defense (MTD) is a proactive security mechanism which changes the attack surface aiming to confuse attackers. Cloud computing leverages MTD techniques to enhance clou…
MSD-Kmeans: A Novel Algorithm for Efficient Detection of Global and Local Outliers
Yuanyuan Wei, Julian Jang-Jaccard, Fariza Sabrina +1
Outlier detection is a technique in data mining that aims to detect unusual or unexpected records in the dataset. Existing outlier detection algorithms have different pros and cons…