Securing Future Networked Infrastructures through Dynamic Normal Behaviour Profiling

通过动态正常行为分析确保未来网络基础设施的安全

基本信息

批准号：
1945273
负责人：
金额：
--
依托单位：
University of Glasgow
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2017
资助国家：
英国
起止时间：
2017 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-1945273
关键词：
Securing Future Networked Infrastructures through

项目摘要

Regular attacks on online services are becoming an ever-present reality. Hackers seek to breach the security of organisations to compromise users and gain confidential information, or deny others access to internet shops and services to inflict monetary harm. System administrators and security researchers are engaged in an unending game of cat-and-mouse with these adversaries, and can often only react to attacks and security holes after the fact. More proactive, automated methods are needed to identify potentially harmful or disruptive traffic as it presents itself.The objective of the research is to explore recent advances in programmable network mechanisms (such as, e.g., Software-Defined Networking and Network Function Virtualisation) together with cutting-edge machine learning techniques to develop distributed, on-the-fly machine analysis and handling of anomalous traffic as deviations from a normal behaviour profile that will be constructed based on the evolving behaviour of network traffic in due course.While the constituent parts of this work are well-understood, the novelty in this research arises from our intended exploration of how machine learning-driven network analysis and programmable networks will interact for the purposes of everyday network management, threat detection and threat control.Given the reality of the threat that cyber attacks pose to modern business and government, this work is particularly timely.The development of more advanced and capable networking systems, statistical models and their intersection will have wide impact, and is expected to potentially benefit UK businesses, universities and institutes by increasing their resilience against common and uncommon adversaries.This work directly aligns with the following ambitions from the EPSRC's prosperity outcomes - R3: "Develop better solutions to acute threats: cyber, defence, financial and health" (Resilient Nation); C1: "Enable a competitive, data driven economy"; C3: "Deliver intelligent technologies and systems"; and C4: "Ensure a safe and trusted cyber society" (Connected Nation).Furthermore, it is directly relevant to the EPSRC growth area "Statistics and applied probability", the maintenance area "Artificial intelligence technologies", and "ICT networks and distributed systems".

对在线服务的定期攻击正在成为永远存在的现实。黑客试图违反组织的安全，以妥协用户并获取机密信息，或者拒绝其他人访问互联网商店和服务以造成货币危害。系统管理员和安全研究人员与这些对手一起从事无休止的猫和小鼠游戏，事实后通常只能对攻击和安全漏洞做出反应。需要采用更积极的自动化方法来确定潜在的有害或破坏性流量，其目的是探索最新的可编程网络机制的进步（例如，例如，软件定义的网络和网络功能虚拟化）（例如，软件定义的网络功能和网络功能虚拟化），以及在开发方面开发的较高的机器学习能力，以开发出来，并在开发方面进行交通。 evolving behaviour of network traffic in due course.While the constituent parts of this work are well-understood, the novelty in this research arises from our intended exploration of how machine learning-driven network analysis and programmable networks will interact for the purposes of everyday network management, threat detection and threat control.Given the reality of the threat that cyber attacks pose to modern business and government, this work is particularly timely.The development of more advanced and capable networking systems, statistical models and their交叉路口将产生广泛的影响，并有望通过提高对普通和罕见的对手的韧性来使英国的企业，大学和机构有可能受益。这项工作直接与EPSRC的繁荣成果的以下雄心相吻合-R3：“对急性威胁发展更好地解决急性威胁：网络，国防，金融，金融和健康”（RECILIENINIAL”（RECILIENINIC”）； C1：“启用竞争性，数据驱动的经济”； C3：“交付智能技术和系统”；和C4：“确保一个安全且值得信赖的网络社会”（相互关联的国家）。furthermore，它与EPSRC增长领域“统计数据和应用概率”直接相关，维护区“人工智能技术”以及“ ICT网络和分布式系统”。