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Securing Future Networked Infrastructures through Dynamic Normal Behaviour Profiling

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
批准号:
1945273
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2017
资助国家:
英国
项目状态:
已结题
起止时间:
2017 至 --

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中文摘要
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英文摘要
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".
期刊论文(4)
专著(0)
科研奖励(0)
会议论文
DOI: 10.1109/noms54207.2022.9789930
发表时间: 2022-04
期刊: NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium
影响因子: --
作者: [Kyle A. Simpson;D. Pezaros]
通讯作者: Kyle A. Simpson;D. Pezaros
Seiðr: Dataplane Assisted Flow Classification Using ML
Seiår:使用 ML 进行数据平面辅助流分类
DOI: 10.1109/globecom42002.2020.9348063
发表时间: 2020
期刊:
影响因子: --
作者: [Simpson K]
通讯作者: Simpson K
Online RL in the programmable dataplane with OPaL
使用 OPaL 在可编程数据平面中进行在线强化学习
DOI: 10.1145/3485983.3493345
发表时间: 2021
期刊:
影响因子: --
作者: [Simpson K]
通讯作者: Simpson K
DOI: 10.1109/tnsm.2019.2960202
发表时间: 2020-03
期刊: IEEE Transactions on Network and Service Management
影响因子: 5.3
作者: [Kyle A. Simpson;S. Rogers;D. Pezaros]
通讯作者: Kyle A. Simpson;S. Rogers;D. Pezaros
海外基金