Discovery Early Career Researcher Award - Grant ID: DE210101458

Funding Activity

Website
http://dataportal.arc.gov.au/NCGP/API/grants/DE210101458

Funding Status
Active

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Funded Activity Summary

Scalable and Deep Anomaly Detection from Big Data with Similarity Hashing. Anomaly detection, aiming to identify anomalous but insightful patterns in data mining, is an important big data analytics technique. The nature of big data requires a detection method that can handle fast-evolving data of diverse types. However, existing methods suffer from either high computational cost or low detection performance. This project aims to develop a detection framework to advance detection performance and efficiency, based on a novel deep learning model called deep isolation forest which is different from the traditional artificial neural network based models. The outcome will bring huge benefits to various applications such as real-time predictive maintenance in smart manufacturing, and intrusion detection in cybersecurity.

Funded Activity Details

Start Date: 2021

End Date: 12-2023

Funding Scheme: Discovery Early Career Researcher Award

Funding Amount: $387,141.00

Funder: Australian Research Council

Research Topics

ANZSRC Field of Research (FoR)

Pattern Recognition and Data Mining | Database Management | Distributed and Grid Systems | Artificial Intelligence and Image Processing

ANZSRC Socio-Economic Objective (SEO)

Expanding Knowledge in the Information and Computing Sciences | Information Processing Services (incl. Data Entry and Capture) |

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