Wei Liang

• Doctor of Engineering
• Professor at Hunan University of Science and Technology

An FPGA (Field Programmable Gate Array) based distributed IP (Intellectual Property) watermarking method is presented for reusable IP protection. The signature is encrypted by the hash algorithm MD5, generating 128 bits digital digest. The digest is then transformed into watermark positions and watermark bits with high security. The watermark bits...

The IP (Intellectual Property) protection has been widely concerned by more semiconductor companies and research institutions, due to the rapid advances in deep sun-micron integrated circuits. A DFA (Deterministic Finite Automaton)-based distributed IP watermarking method is presented by using data compression technology. DFA is used for generating...

A new chaotic map based IP (Intellectual Property) watermarking scheme at physical design level is presented. An encrypted watermark is embedded into the physical layout of a circuit by configuring LUT (Lookup Table) as specific functions when it is placed and routed onto the FPGA (Field-Programmable Gate Array). The main contribution is the use of...

In Very Large Scale Integrated Circuits (VLSI) design, the existing Design-for-Test(DFT) based water-marking techniques usually insert watermark through re-ordering scan cells, which causes large resource overhead, low security and coverage rate of watermark detection. A novel scheme was proposed to watermark multiple scan chains in DFT for solving...

Hierarchical federated learning (HFL) is an effective “cloud-edge-device” distributed model training framework that protects data privacy. During HFL training, poisoning attacks on local data and transmitted models can affect the accuracy of the global model. Existing methods for defending against unauthorized attacks primarily rely on single-featu...

In recent federated learning (FL) research, while significant progress has been made in addressing data and model heterogeneity, the challenges posed by computational heterogeneity in devices remain largely unresolved. In this work, we propose PHFL, a novel FL framework that leverages a hybrid synchronization mechanism to tackle the computational h...

One of the most widely applied tasks in security and computer vision applications today is object detection, by which different categories of objects can be detected and located from images and videos. The advances in Deep Learning (DL) have presented us with new ways to detect objects and develop efficient mechanisms for object detection. The stat...

In Unmanned Aerial Vehicle Ad Hoc Networks (UANETs), rapid movement of nodes leads to frequent changes in network topology, increasing the risk of packet loss and affecting data transmission. Furthermore, current research on drone clustering lacks security considerations, which reduces the reliability of data transmission. Due to this, improving th...

Due to the substantial feature extraction and end-to-end learning capability, deep learning has been widely used in intelligent medical image detection. However, amount of parameters in these models relies on the number of labeled training data, which influences the performance. Due to this reason, we propose a novel unsupervised medical image dete...

Distributed Reflection Denial-of-Service (DRDoS) attacks have caused significant destructive effects by virtue of emerging protocol vulnerabilities and amplification advantages, and their intensity is increasing. The emergence of programmable data plane supporting line-rate forwarding provides a new opportunity for fine-grained and efficient attack...

Low-rate threats are a class of attack vectors that are disruptive and stealthy, typically crafted for security vulnerabilities. They have been the significant concern for cyber security, impacting both conventional IP-based networks and emerging Software-Defined Networking (SDN). SDN is a revolutionary architecture that separates the control and d...

With the rapid growth of the internet of things (IoT) and smart devices, edge computing has emerged as a critical technology for processing massive amounts of data and protecting user privacy. Split federated learning, an emerging distributed learning framework, enables model training without needing data to leave local devices, effectively prevent...

Traffic prediction is crucial for intelligent transportation systems, assisting in making travel decisions, minimizing traffic congestion, and improving traffic operation efficiency. Although effective, existing centralized traffic prediction methods have privacy leakage risks. Federated learning-based traffic prediction methods keep raw data local...

The influence maximization problem that selects a set of seed nodes to maximize the influence spread has been becoming a hot research topic. The classical algorithms select the seed nodes at the initial moment based on the topological properties in static networks, which are not suitable for solving the problem in dynamic networks. In this paper, w...

In recent years, open mobile social networks focused on socializing and dating purposes have gained widespread popularity, such as Soul, Tinder, Momo, and Tantan, among several others. These applications permit users to post, comment, and send private messages to other users without their consent, making communication accessible. However, this low-...

As a typical privacy-aware machine learning paradigm, federated learning (FL) provides facilities to individually train edge clients with their private data and aggregate the central global model. In this way, privacy leakage can be prevented. Massive communication overhead caused by exchanging updated weights between clients and the server is one...

The development of the Internet of Things (IoT), Big Data, and deep learning technologies has brought convenience to people’s lives. As personal privacy data protection laws and regulations tighten, the cost of acquiring high-quality annotated data from vast IoT datasets has significantly increased, resulting in prevalent issues such as data acquis...

Federated learning enables model training for the consumer-driven Internet of Things (IoT) in a distributed manner without violating individual privacy. Several secure aggregation protocols have been proposed for large-scale federated learning models in IoT scenarios. However, the communication and computational overhead grow quadratically with the...

Exploring simple and efficient computational methods for drug repositioning has emerged as a popular and compelling topic in the realm of comprehensive drug development. The crux of this technology lies in identifying potential drug-disease associations, which can effectively mitigate the burdens caused by the exorbitant costs and lengthy periods o...

Blockchain is a decentralized distributed ledger that combines multiple technologies, including chain data structures, P2P networks, consensus algorithms, cryptographic principles, and smart contracts. This gives the blockchain the characteristics of decentralization, immutability, and traceability. However, blockchain stores smart contracts and tr...

The development of the Internet of Things has led to a surge in edge devices. The image detection algorithm, one of the commonly used algorithms in edge computing, is affected by environments such as weather, light, air humidity, smoke, and dust, so an image restoration algorithm is needed to preprocess the image in practice. Most currently propose...

Outdoor images taken in haze usually exhibit contrast reduction, color distortion, and detail loss. Removing the haze from a given image is a tough issue owing to its highly ill-posed property. To restore the haze-free image effectively, we develop an unsupervised dehazing method using patch-line and fuzzy clustering-line priors in this paper. The...

The application of healthcare systems has led to an explosive growth in personal electronic health records (EHRs). These EHRs are generated from different healthcare institutions and stored in cloud data centers, respectively. However, data owners lose the authority to control and track their private and sensitive EHRs. In fact, data owners cannot...

Inferring potential drug indications plays a vital role in the drug discovery process. It can be time-consuming and costly to discover novel drug indications through biological experiments. Recently, graph learning-based methods have gained popularity for this task. These methods typically treat the prediction task as a binary classification proble...

The visual information processing technology based on deep learning (DL) can play many important yet assistant roles for unmanned aerial vehicles (UAV) navigation in complex environments. Traditional centralized architectures usually rely on a cloud server to perform model inference tasks, which can lead to long communication latency. Using transfe...

Machine learning has demonstrated tremendous potential in solving real-world problems. However, with the exponential growth of data amount and the increase of model complexity, the processing efficiency of machine learning declines rapidly. Meanwhile, the emergence of quantum computing has given rise to quantum machine learning, which relies on sup...

In recent years, with the development of cyber security, the national demand for cybersecurity-related professionals is growing, and the practice of cyber security related practices is increasing, which makes it difficult for students to find suitable practices in the massive practice resources, so the practice recommendations method for students e...

Exam is the most effective method to evaluate the quality of higher education, and improving higher education exam quality is of paramount importance. Traditional methods of analyzing and improving higher education exam quality, such as mean and variance based on mathematical statistics, are only suitable for sample datasets that are small and stat...

As the number of Internet of Things edge devices in smart factories increasing, it is crucial to predict the lifetime of the equipment to keep the production running normally. Although predictive maintenance based on machine learning achieve a better performance, they still face the challenge of black box and time-efficient. This paper propose a st...

Impulsive noise is always present in real-world image Compressive Sensing (CS) acquisition systems, where existing CS reconstruction performance may seriously deteriorate. In this paper, we propose a robust CS formulation for image reconstruction to suppress outliers in the presence of impulsive noise. To address this issue, we consider a novel tru...

Accurate traffic prediction is indispensable for relieving traffic congestion and people’s daily trips. Nevertheless, accurate traffic flow prediction is still challenging due to the traffic network’s complex and dynamic spatial and temporal dependencies. Most existing methods usually ignore the dynamicity of spatial dependencies or have limitation...

Federated learning (FL), as an effective method to solve the problem of “data island”, has become one of the hot and widespread concern topics in recent years. However, with the using of FL technology in the practical applications, an increasing number of FL tasks make the training management be more complex and the trade-off of multi-task becomes...

Blockchain technology, with its unique decentralized and tamper-resistant features, is being utilized to address the issue of information silos in traditional electronic healthcare. However, as healthcare data sources become increasingly complex and numerous, the limited scalability and transaction throughput of traditional blockchains result in ch...

The integration of 6G networks with emerging key technologies such as blockchain, artificial intelligence, and digital twins continues to improve. However, it carries many issues with security threats and challenges of 6G networks. In this article, we analyzed the security issues of 6G networks and presented some possible solutions. First, we discu...

Federated learning (FL) is widely used in various fields because it can guarantee the privacy of the original data source. However, in data-sensitive fields such as Internet of Vehicles (IoV), insecure communication channels, semi-trusted RoadSide Unit (RSU), and collusion between vehicles and the RSU may lead to leakage of model parameters. Moreov...

With the swift advancement of the Internet of Things (IoT) and Artificial Intelligence (AI), various technologies have been integrated into wearable medical health devices, improving users’ awareness of their physical states and enabling the analysis of a greater amount of human data. However, these sensitive pieces of information are prone to tamp...

Traffic flow prediction is a non-negligible part of intelligent transportation and mobility. Unfortunately, the unique non-linearity and complex spatial-ST-correlation of transport flow data suggest considerable challenges in prediction. The dynamic interaction of multiple spatial relations greatly influences traffic flow prediction. However, the e...

Data recovery is a fundamental task for sparse network monitoring with a significant impact on many downstream tasks, such as congestion control, network capacity planning, and traffic engineering. To better capture the network dynamically and quickly respond to network failure, network monitoring systems take finer temporal granularity to collect...

The Low-Rate Denial of Service (LDoS) attack poses a significant threat to Internet services. Exploiting vulnerabilities in adaptive mechanisms embedded within network protocols, LDoS attacks are covert and exhibit legal behavior, making defense challenging. Existing LDoS attack solutions cannot perform real-time LDoS attack defense at line speed....

With the expansion of scale, the Internet of Things (IoT) suffers more and more security threats, and vulnerability and sensitivity to attacks are also increasing. As a distributed and secure network architecture, Blockchain is suitable for protecting the security and privacy of the IoT. In this article, we propose a secure smart blockchain IoT arc...

In this paper, we propose a dynamic object elimination algorithm that combines semantic and geometric constraints to address the problem of visual SLAM being easily affected by dynamic feature points in dynamic environments. This issue leads to the degradation of localisation accuracy and robustness. Firstly, we employ a lightweight YOLO-Tiny netwo...

Software-defined networking (SDN) faces challenges in efficiently forwarding packets across the network due to the limited capacity of flow tables in the switches. Ternary content addressable memory (TCAM) is typically used to store flow tables, but its limited capacity makes it vulnerable to attacks. Specifically, the Low-rate Flow Table Overflow...

Slow-rate denial-of-service (SDoS) attacks are a type of denial-of-service (DoS) attacks with a low attack rate. They have a flash-crowd nature and can be well concealed in legitimate traffic, so it is difficult to identify them by anti-DoS mechanisms. Existing solutions have drawbacks such as difficult deployment, poor real-time performance, and p...

The rapid advancement of deep learning has significantly heightened the threats posed by Side-Channel Attacks (SCAs) to information security, transforming their effectiveness to a degree several orders of magnitude superior to conventional signal processing techniques. However, the majority of existing Deep-Learning Side-Channel Attacks (DLSCAs) pr...

The application of healthcare systems has led to an explosive growth in personal electronic health records (EHRs). These EHRs are generated from different healthcare institutions and stored in cloud data centers, respectively. However, data owners lose the authority to control and track their private and sensitive EHRs. In fact, data owners cannot...

Cryptography is very essential in our daily life, not only for confidentiality of information, but also for information integrity verification, non-repudiation, authentication, and other aspects. In modern society, cryptography is widely used; everything from personal life to national security is inseparable from it. With the emergence of quantum c...

The Dual-motor multi-gear coupling powertrain (DMCP) has the potential to improve transmission system efficiency and driving comfort, but its complex structure and multiple working modes present challenges. The switching between different modes is easy to cause longitudinal biggish vehicle jerk. To address these issues,this paper introduces the Dee...

The emergence of numerous consensus algorithms for distributed systems has resulted from the swift advancement of blockchain and its related technologies. Consensus algorithms play a key role in decentralized distributed systems, because all nodes in the system need to reach a consensus on requests or commands through consensus algorithms. In a dis...

Network operation and maintenance rely heavily on network traffic monitoring. Due to the measurement overhead reduction, lack of measurement infrastructure, and unexpected transmission error, network traffic monitoring systems suffer from incomplete observed data and high data sparsity problems. Recent studies model missing data recovery as a tenso...

Food-oriented cross-modal retrieval aims to retrieve relevant recipes given food images or vice versa. The modality semantic gap between recipes and food images (text and image modalities) is the main challenge. Though several studies are introduced to bridge this gap, they still suffer from two major limitations: 1) The simple embedding concatenat...

Deep-learning-as-a-service (DLaaS) has received increasing attention because of its novelty as a diagram for deploying deep learning techniques. However, DLaaS still faces performance and security issues, which must be solved urgently. Given the limited computation resources and concern of benefits, distributed DLaaS systems require quality-of-serv...

Software-defined networking (SDN) is a new network architecture that separates the data plane from the control plane and provides network programmability, dynamic deployment, and management of network traffic. However, its security also faces many threats, such as low-rate denial of service (LDoS) attacks. The LDoS attack can use the vulnerability...

Crowdsourcing takes advantage of human intelligence to solve complex problems that computers cannot handle. People actively participate in computational tasks for rewards, especially those that are relatively simple for humans but challenging for computers. However, most traditional crowdsourcing systems must rely on a central organisation to handl...

With the development of the drying rack system, users with limited time tend to use the fully functional drying rack system to realize various intelligent control functions. However, the existing control methods for drying rack system has some defects such as, low intelligence and system delay, which are unsuitable for most users. In this paper, an...

With the rapid growth in wireless communication and IoT technologies, Radio Frequency Identification (RFID) is applied to the Internet of Vehicles (IoV) to ensure the security of private data and the accuracy of identification and tracking. However, in traffic congestion scenarios, frequent mutual authentication increases the overall computing and...

With the rapid advances in computing and networking technologies, there have led to the creation of a novel and booming set of payment services, known as cryptocurrencies or digital tokens. Many are available for exchanges worldwide, inviting investors to trade with costs, quality, and safety that vary widely. Nevertheless, Blockchain transaction d...

Since the birth of Bitcoin, blockchain has shifted from a critical cryptocurrency technology to an enabling technology. Due to its immutability and trustworthiness, blockchain has revolutionized many fields requiring credibility and high-quality data for decision making. Particularly in business intelligence and business process management, users c...

Natural language processing (NLP) assists to increase the efficiency of human and Multimedia Internet of Things (MIoT) interaction. Notably, large-scale NLP tasks can be offloaded from a cloud server to fog nodes closer to a mobile terminal device for lower response latency. But communication security is ongoing issues that need to be addressed. Ef...

The deep integration of Internet of Medical Things (IoMT) and Artificial intelligence makes the further development of intelligent medical services possible, but privacy leakage and data security problems hinder its wide application. Although the combination of IoMT and federated learning (FL) can achieve no direct access to the original data of pa...

Aiming at the problem of low detection accuracy of network traffic data types by traditional intrusion detection methods, we propose an improved Harris Hawk hybrid intrusion detection method to enhance the detection capability. The improved Harris Hawk optimization algorithm is used as a feature selection scheme to reduce the impact of redundant an...

Nowadays, blockchain distributed ledger technology is becoming more and more prominent, and its decentralization, anonymization, and tampering obvious features have been widely recognized. These excellent technical features of blockchain have also made it a hot issue for global research. With the wide application of blockchain technology in various...

The application of differential privacy (DP) in federated learning can effectively protect users’ privacy from inference attacks. However, privacy budget allocation strategies in most DP schemes not only fail to be applied in complex scenarios but also severely damage the model usability. This paper designs a stochastic gradient descent algorithm b...

A microRNA is a small, single-stranded, non-coding ribonucleic acid that plays a crucial role in RNA silencing and can regulate gene expression. With the in-depth study of miRNA in development and disease, miRNA has become an attractive target for novel therapeutic strategies. Exploring miRNA targeting therapy only through experiments is expensive...

Software-Defined Networking (SDN) switches typically have limited ternary content addressable memory (TCAM) that caches the flow entries on the data plane. The scarcity and strong resource competitiveness of TCAM space put the flow tables at the risk of malicious Distributed Denial-of-Service (DDoS) attacks. In this paper, we propose LtRFT, a Learn...

The Software-Defined Networking (SDN) is a new network framework widely adopted in data center networks that decouples the control plane from data plane to make network management easier. In SDN, OpenFlow is a mainstream southbound communication protocol for controllers and network devices. In an OpenFlow-supported SDN network, the control plane es...

Privacy-preserving federated learning (PPFL) is vital for Industry 5.0 digital ecosystems due to the increasing number of interconnected devices and the volume of shared sensitive data. Secure aggregation (SA) protocols are essential components to fulfill the privacy properties of PPFL. However, there are still fundamental challenges to be tackled....

As known, the smart Grid is an essential scenario for Industry 5.0. With its rapid development, the huge number of sensors and smart devices widely used in the industrial field generate significant amounts of data that sharply increase. Facing the power grid environment with a high amount of data, it is easy to cause abnormal conditions in the powe...

As one of the key components of Web 3.0, the security of cryptocurrency is essential to its development and application. A Payment Channel Network (PCN) enhances transaction confirmation speed and throughput by executing blockchain transactions off-chain. However, existing PCN protocols have problems like privacy leaks and offline and challenges in...

The payment channel network aims to solve the problems of long payment confirmation time and limited throughput in cryptocurrencies through off-chain payments. Hash Time-Lock Contract (HTLC) is an off-chain payment protocol that Lightning Network (LN) adopted. Unfortunately, when performing high-valued payments off-chain, due to the impact of payme...

When it comes to running and managing modern supply chains, 6G Internet of things (IoT) is of utmost importance. To provide IoT with security and automation, blockchain and machine learning are two upper-layer technology that can help. First, we propose to utilize blockchain in modern supply chains to ensure efficient collaboration between all part...

Mobile edge computing (MEC) can enhance the computation capabilities of smart mobile devices for computation-intensive mobile applications via supporting computation offloading efficiently. However, the limitation of wireless resources and computational resources of edge servers often becomes the bottlenecks to realizing the developments of MEC. In...