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FORENSIC: Fast and Autonomous Platform Anomalies detections in Cyber Physical Systems

FORENSIC：网络物理系统中的快速自主平台异常检测

基本信息

批准号：
10077013
负责人：
金额：
$ 4.08万
依托单位：
UNIVERSITY OF ESSEX
依托单位国家：
英国
项目类别：
Collaborative R&D
财政年份：
2023
资助国家：
英国
起止时间：
2023 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=10077013
关键词：
FORENSIC Fast Autonomous Platform Anomalies

项目摘要

Market Need:Critical systems, such as the power grid, autonomous transportation, process control, and IoT systems, are examples of cyber-physical systems (CPS). Such CPS are liable to be attacked by malicious agents who can compromise the sensor measurements used to control them. Nevertheless, industries still rely on traditional software-based security mechanisms to tackle attack scenarios. A recent survey found that the current cybersecurity strategy would likely be outdated in two years because the mitigation actions generated are usually generic and difficult to put into context for mission-critical CPS.Solution:FORENSIC is a hardware-based solution that rapidly and autonomously monitors the health of the systems by collecting hardware data that are hard to compromise for threat detection. It utilises innovative technology to raise an alert for anomalies, if it observes deviations in the system's health. In addition, it is quicker, cheaper, and less power-consuming than its rivals. The system detects any operational changes in the systems iteratively and autonomously when an attacker might reach the critical component of the system by comparing the runtime collected data with "known good" baseline data.Target Markets:UK-based smart manufacturers and critical infrastructure providersAutomotive and healthcareSecurity solution providers within Industry 4.0

市场需求：关键系统，如电网，自动运输，过程控制和物联网系统，是网络物理系统（CPS）的例子。这样的CPS容易受到恶意代理的攻击，恶意代理可以损害用于控制它们的传感器测量。然而，行业仍然依赖传统的基于软件的安全机制来应对攻击场景。最近的一项调查发现，当前的网络安全策略可能会在两年内过时，因为所产生的缓解措施通常是通用的，很难放在任务关键型CPS的背景下。解决方案：FORENSIC是一种基于硬件的解决方案，通过收集难以妥协的硬件数据进行威胁检测，快速自主地监控系统的健康状况。它利用创新技术，如果观察到系统健康状况的偏差，就会发出异常警报。此外，它比竞争对手更快，更便宜，耗电量更少。当攻击者可能到达系统的关键组件时，系统通过将运行时收集的数据与“已知良好”的基线数据进行比较，迭代并自主地检测系统中的任何操作更改。目标市场：英国的智能制造商和关键基础设施提供商汽车和医疗行业工业4.0中的安全解决方案提供商