Cyber Resilience in Healthcare Digital Twin on Lung Cancer

Abstract

As a key service of the future 6G network, healthcare digital twin is the virtual replica of a person, which employs Internet of Things (IoT) technologies and AI-powered models to predict the state of health and provide suggestions to a range of clinical questions. To support healthcare digital twins, the right cyber resilience technologies and policies must be applied and maintained to preserve cyber resilience. Vulnerability detection is a fundamental technology for cyber resilience in healthcare digital twins. Recently, deep learning (DL) has been applied to address the limitations of traditional machine learning in vulnerability detection. However, it is important to consider code context relationships and pay attention on the vulnerability related keywords for searching an IoT vulnerability in healthcare digital twins. Due to massive software and complexity of healthcare digital twin, a full automatic solution is really needed for assisting cyber resilience check in the real-world scenarios. This paper presents a novel scheme for recognising potential vulnerable functions to support healthcare digital twins. We develop a new deep neural model to capture bi-directional context relationships among the risky code keywords. A number of well-designed experiments are carried out on a large ground truth, which consists of tens of thousands of vulnerable and non-vulnerable functions from IoT related software. The results show our new scheme outperforms the state-of-the-art DL-based methods for vulnerability detection. INDEX TERMSInternet of Things (IoT), healthcare, digital twin, cyber resilience, lung cancer VOLUME xxx, 20xx

Digital Twin Perspective of Fourth Industrial and Healthcare Revolution

“…The correct cyber resilience technology and policy are important to maintain and preserve a healthcare digital twin. Authors of [207] pointed toward vulnerability detection as an essential technology for cyber resilience in healthcare DT. Deep Learning (DL) is implemented to overcome the limitation of machine learning in vulnerability detection.…”

Reliable and Resilient AI and IoT-Based Personalised Healthcare Services: A Survey

“…Any disruption of the services due to intentional or accidental incidents may also threaten patients' lives. To establish resilience, various efforts [15], [16] have recognized potentially vulnerable functions (e.g., failure of software, hardware, cloud services, and communication devices) that may hinder the resilience of healthcare attributes and proposed different ways to recover from them (e.g., post-event automatic recovery of the vulnerabilities like Moving Target Defense (MTD) [17], and blockchain-based solutions [18]). • Personalization: Personalized healthcare services typically operate in the most strict mode by supporting customization of a specific health condition under specific conditions [19], [20].…”

“…), and are not clinically personalized (i.e., unaware for the other associated medical conditions of the patient). AI/ML-based approaches provides personalization in specific environment [96] and for specific diseases [15].…”

Towards a Novel Framework for Reinforcing Cybersecurity using Digital Twins in IoT-based Healthcare Applications

“…Several authors have discussed the applicability of DT to be used for enhancing cybersecurity in different aspects such as cyberattack prediction [2], vulnerability detection & cyber resilience [4], data security and privacy [6], and cyber-range [7]. Fig.…”

“…DT communicates with the physical world without altering anything in both worlds [2]. This brings several benefits to different applications, particularly in the IoT-based healthcare applications, which saves costs, predicts potential threats, and enhances decision-making processes [3] [4]. COVID-19 has witnessed a need for DT in public and private sectors since virtual interaction has been significant for smooth operations [5].…”