Cyber Risk In The Financial System

虽然物联网的兴起正在迅速扩展商业技术的数字化格局，但企业变得高度暴露于信息风险，并且更加担心与其基础设施和环境相关的网络安全问题。由于数字化在信贷中介活动中的核心作用，金融部门是网络犯罪最有针对性的部门之一。网络安全正在演变成一个全球性的经济问题，而不仅仅是一个技术风险。据世界经济论坛报道，欺诈和金融犯罪是一个价值数万亿美元的产业。因此，需要对金融网络安全事件及其影响进行适当建模、解释和处理。该项目旨在开发一个强大的建模框架，以评估和量化网络风险，并分配资源来管理金融系统的网络安全。它培养了一个具有信息技术，银行，会计和金融，数据科学等知识和技能的候选人，特别是博士研究项目整合了计算机科学（例如人工智能，深度学习），金融，系统工程和数学的前沿方法。它是跨学科的，旨在涵盖金融业面临的网络安全问题的以下方面：使用深度学习算法检测金融系统中的安全事件;使用霍克斯过程来模拟网络攻击和欺诈活动的传染性特征;定义和量化金融系统的脆弱性和不稳定性;建立金融网络风险评级模型。

While the rising of the Internet of Things is rapidly expanding the digital landscape of business technology, corporations become highly exposed to information risks and more concerned with cybersecurity issues related to their infrastructure and environment. The financial sector is one of the most targeted sectors by cybercrime due to the central role of digitization in the credit intermediation activities. Cybersecurity is evolving into a global-wide economic issue rather than only a technology risk. As reported by the World Economic Forum, the fraud and financial crime is a trillion-dollar industry. Hence, financial cybersecurity events and their impacts need to be properly modelled, interpreted and handled. This project aims at developing a robust modelling framework to assess and quantify cyber risk and to allocate resources to manage cybersecurity in the financial system. It develops a candidate with knowledge and skills in information technology, banking, accounting and finance, data science, etc. Especially, the doctoral research project integrates cutting-edge methodologies in computer science (e.g. Artificial intelligence, deep learning), finance, system engineering and mathematics. It is interdisciplinary and aims to cover the following aspects that centre on cybersecurity issues faced by the financial industry:detecting security incidents in the financial system using deep learning algorithms;using Hawkes processes to model the contagious feature of cyberattacks and fraud activities;defining and quantifying the financial system vulnerability and instability;building financial cyber-risk rating models.