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

• 批准号: 10097853
• 金额: $ 7.62万
• 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=10097853
• 关键词: FORENSIC; Fast; Autonomous; Platform; Anomalies

**Market Need**Critical systems, such as the power grid, autonomous transportation, industrial robots, and process control, are examples of cyber-physical systems (CPS). Recently, in the second half of 2022, there has been an 80% increase in cyberattacks on such devices. Most interestingly, in 54% of cases, these attacks occur due to unauthorised code or commands silently executed in the systems and compromising the sensor measurements used to control them. When such an attack occurs, the CPS sector faces a huge monetary loss, approximately £20K per minute. Nevertheless, industries still rely on traditional software-based intrusive 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 A recent survey found that the current cybersecurity strategy would likely be outdated in two years because the mitigation actions generated are usually generic and difficult to put into context for mission-critical CPS. Additionally, CPS devices are often deployed in remote or physically inaccessible locations. Applying security updates and patches to these devices can be challenging.**Solution**FORENSIC is a hardware-software co-design-based solution that rapidly and autonomously monitors the health of the systems by collecting low-level hardware features from the target device 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 employing novel innovative AI techniques. Compared to existing solutions, the unique selling point of FORENSIC is that runtime threat detection is based on robust hardware features that are difficult to compromise. Another unique feature of FORENSIC is that it does not rely on the modelling of the software applications running on the platforms and can also be easily calibrated and adapted to different execution platforms.**Target Markets:*** UK-based smart manufacturers and critical infrastructure providers* Automotive industry and healthcare* Security solution providers within Industry 4.0

关键系统，如电网、自动运输、工业机器人和过程控制，都是网络物理系统（CPS）的例子。最近，在2022年下半年，针对此类设备的网络攻击增加了80%。最有趣的是，在54%的情况下，这些攻击是由于未经授权的代码或在系统中静默执行的命令而发生的，并危及用于控制它们的传感器测量。当这种攻击发生时，CPS部门面临着巨大的经济损失，大约每分钟2万英镑。然而，行业仍然依赖传统的基于软件的侵入式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。此外，CPS设备通常部署在远程或物理上无法访问的位置。对这些设备应用安全更新和补丁可能具有挑战性。**解决方案**FORENSIC是一种基于硬件和软件协同设计的解决方案，通过从目标设备收集难以危及威胁检测的低级硬件功能，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、更省电。当攻击者可能到达系统的关键组件时，系统通过采用新颖的创新人工智能技术，迭代和自主地检测系统中的任何操作变化。与现有解决方案相比，FORENSIC的独特卖点在于运行时威胁检测基于难以破坏的健壮硬件特性。FORENSIC的另一个独特之处在于，它不依赖于在平台上运行的软件应用程序的建模，也可以很容易地校准和适应不同的执行平台。**目标市场：***英国智能制造商和关键基础设施提供商*汽车行业和医疗保健*工业4.0中的安全解决方案提供商