Learning the Focus of Attention to Detect Distributed Coordinated Attacks

学习注意力焦点以检测分布式协调攻击

• 批准号: DP200101960
• 负责人: Prof Christopher Leckie
• 金额: $ 33.48万
• 依托单位: The University of Melbourne
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2020
• 资助国家: 澳大利亚
• 起止时间: 2020-08-18 至 2024-04-30
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP200101960
• 关键词: Learning; Focus; Attention; Detect; Distributed

Cyber security analysts need to detect and respond to attacks as soon as possible, to minimise the damage attackers can inflict. However, the growth in highly distributed attacks that span multiple networks has meant that massive volumes of data need to be analysed. While machine learning techniques can help filter the data, we need techniques that can automatically provide a focus of attention for analysts on the most relevant observations. Our aim is to devise a novel suite of attention mechanisms that can focus the search of machine learning techniques for cyber security. The results of this project will improve the accuracy and efficiency of detecting distributed attacks across multiple networks.

网络安全分析师需要尽快发现并应对攻击，以尽量减少攻击者可能造成的损害。然而，跨越多个网络的高度分布式攻击的增长意味着需要分析大量数据。虽然机器学习技术可以帮助过滤数据，但我们需要能够自动为分析师提供最相关观察的关注焦点的技术。我们的目标是设计一套新颖的注意力机制，可以集中搜索用于网络安全的机器学习技术。该项目的研究结果将提高跨多个网络检测分布式攻击的准确性和效率。