Today’s cyber attacks have become more severe and frequent, which calls for a new line of security defenses to protect against them. The dynamic nature of new-generation threats, which are evasive, resilient, and complex, makes traditional security systems based on heuristics and signatures struggle to match. Organizations aim to gather and share real-time cyber threat information and then turn it into threat intelligence for preventing attacks or, at the very least, responding quickly in a proactive manner. Cyber Threat Intelligence (CTI) mining, which uncovers, processes, and analyzes valuable information about cyber threats, is booming. However, most organizations today mainly focus on basic use cases, such as integrating threat data feeds with existing network and firewall systems, intrusion prevention systems, and Security Information and Event Management systems (SIEMs), without taking advantage of the insights that such new intelligence can deliver. In order to make the most of CTI so as to significantly strengthen security postures, we present a comprehensive review of recent research efforts on CTI mining from multiple data sources in this article. Specifically, we provide and devise a taxonomy to summarize the studies on CTI mining based on the intended purposes (i.e., cybersecurity-related entities and events, cyber attack tactics, techniques and procedures, profiles of hackers, indicators of compromise, vulnerability exploits and malware implementation, and threat hunting), along with a comprehensive review of the current state-of-the-art. Lastly, we discuss research challenges and possible future research directions for CTI mining.

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 article 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.

Exploitable vulnerabilities in software have attracted tremendous attention in recent years because of their potentially high severity impact on computer security and information safety. Many vulnerability detection methods have been proposed to aid code inspection. Among these methods, there is a line of studies that apply machine learning techniques and achieve promising results. This paper reviews 22 recent studies that adopt deep learning to detect vulnerabilities, aiming to show how they utilize state-of-the-art neural techniques to capture possible vulnerable code patterns. Among reviewed studies, we identify four game changers that significantly impact the domain of deep learning-based vulnerability detection and provide detailed reviews of the insights, ideas, and concepts that the game changers have brought to this field of interest. Based on the four identified game changers, we review the remaining studies, presenting their approaches and solutions which either build on or extend the game changers, and sharing our views on the future research trends. We also highlight the challenges faced in this field and discuss potential research directions. We hope to motivate the readers to conduct further research in this developing but fast-growing field.

This article presents a novel extended reality (XR) and deep-learning-based Internet-of-Medical-Things (IoMT) solution for the COVID-19 telemedicine diagnostic, which systematically combines virtual reality/augmented reality (AR) remote surgical plan/rehearse hardware, customized 5G cloud computing and deep learning algorithms to provide real-time COVID-19 treatment scheme clues. Compared to existing perception therapy techniques, our new technique can significantly improve performance and security. The system collected 25 clinic data from the 347 positive and 2270 negative COVID-19 patients in the Red Zone by 5G transmission. After that, a novel auxiliary classifier generative adversarial network-based intelligent prediction algorithm is conducted to train the new COVID-19 prediction model. Furthermore, The Copycat network is employed for the model stealing and attack for the IoMT to improve the security performance. To simplify the user interface and achieve an excellent user experience, we combined the Red Zone’s guiding images with the Green Zone’s view through the AR navigate clue by using 5G. The XR surgical plan/rehearse framework is designed, including all COVID-19 surgical requisite details that were developed with a real-time response guaranteed. The accuracy, recall, F ₁ -score, and area under the ROC curve (AUC) area of our new IoMT were 0.92, 0.98, 0.95, and 0.98, respectively, which outperforms the existing perception techniques with significantly higher accuracy performance. The model stealing also has excellent performance, with the AUC area of 0.90 in Copycat slightly lower than the original model. This study suggests a new framework in the COVID-19 diagnostic integration and opens the new research about the integration of XR and deep learning for IoMT implementation.

With the integration of photovoltaic (PV) power into an electrical network, the complexity of the grid management is increasing because of intermittent and fluctuation nature of solar energy. Solar irradiance forecasting is essential to facilitate planning and managing electricity generation and distribution in smart grid cyber-physical system (CPS). The performance of existing short-term forecasting methods is far from satisfactory due to a lack of reliable and fast time-frequency model for continuous-time solar irradiance data. To address this problem, this paper proposes a new method, Elman Neural Network (ENN) driven Wavelet Transform (WT-ENN), for hourly solar irradiance forecasting. Firstly, the solar irradiance series was decomposed into a set of constitutive series using wavelet transform. Secondly, the new wavelet coefficients were predicted by ENNs in every sub-series with the best network structure and parameters. Thirdly, Wavelet reconstruction will predict next hour solar irradiance through the aggregation of outputs of the ensemble of ENNs. Finally, the forecasting performance was evaluated using two large real-world solar irradiance datasets. Experiment results show that the new WT–ENN model outperforms a large number of alternative methods and an average forecast skill of 0.7590 over the persistence model. Thus, it is concluded that the proposed approach can significantly improve the forecasting accuracy and reliability.

A digital twin-enabled Internet of Medical Things (IoMT) system for telemedical simulation is developed, systematically integrated with mixed reality (MR), 5G cloud computing, and a generative adversarial network (GAN) to achieve remote lung cancer implementation. Patient-specific data from 90 lung cancer with pulmonary embolism (PE)-positive patients, with 1372 lung cancer control groups, were gathered from Qujing and Dehong, and then transmitted and preprocessed using 5G. A novel robust auxiliary classifier generative adversarial network (rAC-GAN)-based intelligent network is employed to facilitate lung cancer with the PE prediction model. To improve the accuracy and immersion during remote surgical implementation, a real-time operating room perspective from the perception layer with a surgical navigation image is projected to the surgeon’s helmet in the application layer using the digital twin-based MR guide clue with 5G. The accuracies of the area under the curve (AUC) of our new intelligent IoMT system were 0.92, and 0.93. Furthermore, the pathogenic features learned from our rAC-GAN model are highly consistent with the statistical epidemiological results. The proposed intelligent IoMT system generates significant performance improvement to process substantial clinical data at cloud centers and shows a novel framework for remote medical data transfer and deep learning analytics for digital twin-based surgical implementation.