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

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，都是网络物理系统（CPS）的例子。这样的CPS容易受到恶意代理的攻击，恶意代理可以破坏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处理攻击场景。最近的一项调查发现，目前的网络安全战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，很难将其纳入关键任务CPS的背景中。解决方案：FORENSIC是一种基于硬件的解决方案，它通过收集难以泄露的硬件数据来快速、自主地监视系统的健康状况，以进行威胁检测。它利用创新技术，如果观察到系统健康状况的偏差，就会对异常情况发出警报。此外，它比竞争对手更快、更便宜、更省电。当攻击者可能到达系统的关键组件时，系统通过比较运行时收集的数据与“已知的良好”基线数据，迭代和自主地检测系统中的任何操作更改。目标市场：英国智能制造商和关键基础设施提供商；工业4.0中的汽车和医疗安全解决方案提供商

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 A recent survey found that the current cybersecurity strategy would likely be outdated in two years because the mitigation actions generated are usually generic and difficult to put into context for mission-critical CPS.Solution:FORENSIC is a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