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EPSRC DTP studentship in Cyber Security Analytics: Machine learning threat detection for low complexity edge deployment: threats, risks and mitigation

EPSRC DTP studentship in Cyber Security Analytics: Machine learning threat detection for low complexity edge deployment: threats, risks and mitigation
EPSRC DTP 网络安全分析学生资助:低复杂性边缘部署的机器学习威胁检测:威胁、风险和缓解措施
批准号:
2599523
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:
2021
资助国家:
英国
项目状态:
未结题
起止时间:
2021 至 --

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英文摘要
The deployment of IoT/edge devices is becoming increasing prevalent across society, including in critical national infrastructure (e.g. smart energy). These devices are vulnerable to a wide range of cyber attacks due to their diversity (many manufacturers and protocols) and lack of security mechanisms due to limited compute resource (low processing power). While recent advancements have seen machine learning used to protect such devices from cyber attacks, the deployment of protective models on the devices themselves is often limited due to the lack of compute resource on these lightweight devices.This PhD will generate threats based around the MITRE ATT&CK adversarial threat modelling framework, and produce new methods for edge-device community risk scoring - including suggestions around mitigating controls. These will be tested in a real-world testbed at Toshiba's research labs in Bristol. We are seeking an open minded, creative individual to join the team and develop world class research outcomes with industrial applications. You will join the ESPRC DTP Hub in Cyber Security Analytics at Cardiff University, becoming part of an interdisciplinary cohort of students studying the human and algorithmic aspects of AI in the context of cybersecurity.
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