Technical Reports

Report Date Covered Start
Report Date Covered End
Report Submission Date
Project Implementation Countries
Project Leaders
Chen Lin
Team Members
Denis O'Sullivan [email protected]
Jing Yang [email protected]
Partner Organizations
Total Budget Approved

Experiment and improve reinforcement learning algorithms to enhance anomalous network behaviour detection

TeleMARS Pty Ltd

Cybersecurity is a significant research area because all of the operations based on government, military, commercial, financial and civilians gather, process, transfer and store tremendous volume of data on computers and others. Cyber-attacks have imposed increasing threats and damages on our modern society at all levels. Network Intrusion Detection System (NIDS) is one of the major techniques in preventing cyber-attacks occurred in network traffic. Over the past decade, a lot of research work has been conducted to explore the capabilities of artificial intelligence (AI) methods in developing NIDS solutions. The previous studies suggested that AI algorithms have promising potentials in developing effective solutions to detect the increasing attacks.  TeleMARS R&D team commits to advance AI-based methods, explore realistic approaches of deploying the research outcomes in real network environment, and support on-going research in wider community to achieve long term sustainable development. The key objectives of this project are to contribute to the development of NIDS; contribute to research community in the subject of anomaly detection; establish a practical collaboration framework to enable scientists and IT professionals from diverse background to work together to continuously contribute to NIDS research; test and prove TeleMARS operation and technical frameworks, and the team capabilities; and inspire and enable the participation of broader research community in cybersecurity domain supporting gender equality and inclusion. This project was commenced in September 2020 and finalised in June 2021. The main activities included: Literature review and project design. Data analysis and preparation. Anomaly detection model development using Machine Learning methods including Reinforcement Learning method. Model experimentation. Established evaluation pipelines to simulate real application environment. Model capability evaluation applying different datasets. Implementation of a collaboration framework supporting the research activities conducted by researchers and professionals with various backgrounds.