Hai Dong

RMIT University | RMIT · School of Computing Technologies

Federated Learning (FL) achieves great popularity in the Internet of Things (IoT) as a powerful interface to offer intelligent services to customers while maintaining data privacy. Under the orchestration of a server, edge devices (also called clients in FL) collaboratively train a global deep-learning model without sharing any local data. Neverthe...

We propose a novel approach to effectively detect cloned identities of social-sensor cloud service providers (i.e. social media users) in the face of incomplete non-privacy-sensitive profile data. Named ICD-IPD, the proposed approach first extracts account pairs with similar usernames or screen names from a given set of user accounts collected from...

Although many tools have been developed to detect anomalies in smart contracts, the evaluation of these analysis tools has been hindered by the lack of adequate anomalistic real-world contracts (i.e., smart contracts with addresses on Ethereum to achieve certain purposes). This problem prevents conducting reliable performance assessments on the a...

This paper presents a novel probabilistic Quality of Service (QoS) monitoring method named DLSTM-BRPM (Double Long Short Term Memory (DouLSTM-Den) based Bayesian Runtime Proactive Monitoring) to accurately and efficiently monitor QoS in a mobile edge environment. This method consists of a DouLSTM-Den model and a Gaussian Hidden Bayesian classifier....

With the widespread application of smart contracts, there is a growing concern over the quality assurance of smart contracts. The data flow testing is an important technology to ensure the correctness of smart contracts. We propose an approach named IABC‐TCG (Improved Artificial Bee Colony‐Test Case Generation) to generate test cases for the data f...

The growing popularity of smart contracts in various areas, such as digital payments and the Internet of Things, has led to an increase in smart contract security challenges. Researchers have responded by developing vulnerability detection tools. However, the effectiveness of these tools is limited due to the lack of authentic smart contract vulner...

In recent years, the widespread adoption of distributed microservice architectures within the industry has significantly increased the demand for enhanced system availability and robustness. Due to the complex service invocation paths and dependencies at enterprise-level microservice systems, it is challenging to locate the anomalies promptly durin...

Deep neural networks (DNNs) have been widely and successfully adopted and deployed in various applications of speech recognition. Recently, a few works revealed that these models are vulnerable to backdoor attacks, where the adversaries can implant malicious prediction behaviors into victim models by poisoning their training process. In this paper,...

We propose an innovative approach named MEC-RDESN /mek”r:dI'saIn/ ( MEC QoS forecasting based on R egion recognition and D ynamic E cho S tate N etwork) enabling mobility-aware and swift QoS forecasting in the mobile edge computing environment. MEC-RDESN offers efficient QoS forecasting while maintaining high accuracy. We can identify th...

Federated learning provides privacy protection to the collaborative training of global model based on distributed private data. The local private data is often in the presence of long-tailed distribution in reality, which downgrades the performance and causes biased results. In this paper, we propose a dynamic adaptive federated learning optimizati...

We propose a novel approach to effectively detect cloned identities of social-sensor cloud service providers (i.e. social media users) in the face of incomplete non-privacy-sensitive profile data. Named ICD-IPD, the proposed approach first extracts account pairs with similar usernames or screen names from a given set of user accounts collected from...

With the rise of 5G technology, Mobile (or Multi-Access) Edge Computing (MEC) has become crucial in modern network architecture. One key research area is the effective placement of edge nodes, which has attracted significant attention. Service providers strive to minimize deployment costs for these nodes within a network. Although many studies have...

Given that smart contracts execute transactions worth hundreds of millions of dollars daily, the issue of smart contract security has attracted considerable attention over the past few years. Traditional methods for detecting vulnerabilities heavily rely on manually developed rules and features, leading to the problems of low accuracy, high false p...

Mobile devices commonly offload latency-sensitive applications to edge servers to meet low-latency requirements. However, existing studies overlook dependency and application hit ratio considerations, hindering effective offloading for multi-applications and multi-tasks. To this end, this paper proposes a Dependent task offloading and Service place...

Supply chain finance is a financing scheme to provide financial services for the enterprises of supply chain. Blockchain technology, with distributed, traceable and non-tamperable characteristics, offers opportunities to solve problems in the development of supply chain finance. Consensus algorithms are the key to ensuring the efficient cooperation...

With the rapid development of blockchain platforms, such as Ethereum and Hyperledger Fabric, blockchain technology has been widely applied in various domains. However, various scams exist in the cryptocurrency transactions on the blockchain platforms, which has seriously obstructed the development of blockchain. Therefore, many researchers have stu...

With the rising prominence of smart contracts, security attacks targeting them have increased, posing severe threats to their security and intellectual property rights. Existing simplistic datasets hinder effective vulnerability detection, raising security concerns. To address these challenges, we propose BiAn , a source code level smart contract...

The widespread adoption of DNNs in NLP software has highlighted the need for robustness. Researchers proposed various automatic testing techniques for adversarial test cases. However, existing methods suffer from two limitations: weak error-discovering capabilities, with success rates ranging from 0% to 24.6% for BERT-based NLP software, and time i...

Deep neural networks (DNNs) have been widely and successfully adopted and deployed in various applications of speech recognition. Recently, a few works revealed that these models are vulnerable to backdoor attacks, where the adversaries can implant malicious prediction behaviors into victim models by poisoning their training process. In this paper,...

Mobile Edge Computing (MEC)-based Internet of Things (IoT) systems generate trust information in a real-time and distributed manner. Predicting trustworthiness of IoT services in such an MEC environment requires new prediction strategies that cater for the aforementioned characteristics of trust information. More importantly, it is imperative to in...

Test case generation techniques based on adversarial examples are commonly used to enhance the reliability and robustness of image‐based and text‐based machine learning applications. However, efficient techniques for speech recognition systems are still absent. This paper proposes a family of methods that generate targeted adversarial examples for...

Blockchain has been used in several domains. However, this technology still has major limitations that are largely related to one of its core components, namely the consensus protocol/algorithm. Several solutions have been proposed in literature and some of them are based on the use of Machine Learning (ML) methods. The ML-based consensus algorithm...

Storage systems using Peer-to-Peer (P2P) architecture are an alternative to the traditional client-server systems. They offer better scalability and fault tolerance while at the same time eliminate the single point of failure. The nature of P2P storage systems (which consist of heterogeneous nodes) introduce however data placement challenges that c...

Keyword spotting (KWS) based on deep neural networks (DNNs) has achieved massive success in voice control scenarios. However, training of such DNN-based KWS systems often requires significant data and hardware resources. Manufacturers often entrust this process to a third-party platform. This makes the training process uncontrollable, where attacke...

This article presents a novel probabilistic QoS (Quality of Service) monitoring approach called LSTM-BSPM (DonLSTM-Den based BayeSian Runtime Proactive Monitoring), which is based on the DouLSTM-Den model and Gaussian Hidden Bayesian Classifier for mobile edge environments. A DouLSTM-Den model is designed to predict a user’s trajectory in mobile ed...

Keyword spotting (KWS) has been widely used in various speech control scenarios. The training of KWS is usually based on deep neural networks and requires a large amount of data. Manufacturers often use third-party data to train KWS. However, deep neural networks are not sufficiently interpretable to manufacturers, and attackers can manipulate thir...

With the breakthroughs in Deep Learning, recent years have witnessed a massive surge in Artificial Intelligence applications and services. Meanwhile, the rapid advances in Mobile Computing and Internet of Things has also given rise to billions of mobile and smart sensing devices connected to the Internet, generating zettabytes of data at the networ...

Smart contracts are commonly deployed for safety-critical applications, the quality assurance of which has been a vital factor. Test cases are standard means to ensure the correctness of data flows in smart contracts. To more efficiently generate test cases with high coverage, we propose an improved genetic algorithm-based test-case generation appr...

Big data-driven technologies, especially machine learning and deep learning technologies, have been extensively employed in mineral prospectivity prediction. Several approaches have been proposed to learn the deep characteristics of geoscience data, enhance the accuracy of prediction and reduce uncertainty. Nevertheless, the approaches always conta...

With the rapid development of 5G technologies, the demand of quality of service (QoS) from edge users, including high bandwidth and low latency, has increased dramatically. QoS within a mobile edge network is highly dependent on the allocation of edge users. However, the complexity of user movement greatly challenges edge user allocation, leading t...

We propose a novel privacy-aware Quality of Service (QoS) forecasting approach in the mobile edge environment Edge-PMAM (Edge QoS forecasting with Public Model and Attention Mechanism). Edge-PMAM can make real-time, accurate and personalized QoS forecasting on the premise of user privacy preservation. Edge-PMAM comprises a public model for privacy-...

We propose a novel software service recommendation model to help users find their suitable repositories in GitHub. Our model first designs a novel context-induced repository graph embedding method to leverage rich contextual information of repositories to alleviate the difficulties caused by the data sparsity issue. It then leverages sequence infor...

Short-term rainfall forecasting plays a critical role in meteorology, hydrology, and other related areas. Currently, data-driven approaches have made considerable progress in rainfall forecasting. However, these approaches suffer from the following major drawbacks. First, they do not accommodate a reasonable and effective model to mathematically re...

Edge node placement optimization has been an emerging research area that has drawn extraordinary attention from the disciplines of distributed and services computing. Existing studies, nevertheless, barely focus on overall deployment cost minimization with edge node site selection and server amount optimization, while bearing users’ delay tolerance...

We propose a novel software service recommendation model to help users find their suitable repositories in GitHub. Our model first designs a novel context-induced repository graph embedding method to leverage rich contextual information of repositories to alleviate the difficulties caused by the data sparsity issue. It then leverages sequence infor...

We propose a novel method to detect identity cloning of social-sensor cloud service providers to prevent the detrimental outcomes caused by identity deception. This approach leverages non-privacy-sensitive user profile data gathered from social networks and a powerful deep learning model to perform cloned identity detection. We evaluated the propos...

Deep Neural Network (DNN) driven technologies have been extensively employed in various aspects of our life. Nevertheless, the applied DNN always fails to detect erroneous behaviors, which may lead to serious problems. Several approaches have been proposed to enhance adversarial examples for automatically testing deep learning (DL) systems, such as...

We propose a novel method to detect identity cloning of social-sensor cloud service providers to prevent the detrimental outcomes caused by identity deception. This approach leverages non-privacy-sensitive user profile data gathered from social networks and a powerful deep learning model to perform cloned identity detection. We evaluated the propos...

We propose a data-driven distributed machine learning approach to scalably predict the trustworthiness of homogeneous IoT services in heterogeneous Mobile Edge Computing (MEC)-based IoT systems. The proposed approach formulates training distributed trust prediction models within an MEC-based IoT system as a Network Lasso problem. We then introduce...

Social sensing is a paradigm that allows crowdsourcing data from humans and devices. This sensed data (e.g. social network posts) can be hosted in social-sensor clouds (i.e. social networks) and delivered as social-sensor cloud services (SocSen services). These services can be identified by their providers' social network accounts. Attackers intrud...

The rapid development of social networking platforms in recent years has made it possible for scholars to find partners who share similar research interests. Nevertheless, this task has become increasingly challenging with the dramatic increase in the number of scholar users over social networks. Scholar recommendation has recently become a hot top...

We propose a novel framework that detects conflicts in IoT-based smart homes. Conflicts may arise during interactions between the resident and IoT services in smart homes. We propose a generic knowledge graph to represent the relations between IoT services and environment entities. We also profile a generic knowledge graph to a specific smart home...

Social media have been growing rapidly and become essential elements of many people’s lives. Meanwhile, social media have also come to be a popular source for identity deception. Many social media identity deception cases have arisen over the past few years. Recent studies have been conducted to prevent and detect identity deception. This survey an...

Social media have been growing rapidly and become essential elements of many people's lives. Meanwhile, social media have also come to be a popular source for identity deception. Many social media identity deception cases have arisen over the past few years. Recent studies have been conducted to prevent and detect identity deception. This survey an...

Mobile edge computing is a new computing paradigm that performs computing on the edge of a network. It provides services to users by deploying edge servers near mobile devices. Services may be unavailable or do not satisfy the needs of users due to changing edge environments. Quality of service (QoS) is commonly employed as a critical means to indi...

The quick retrieval of target information from a massive amount of information has become a core research area in the field of information retrieval. Semantic information retrieval provides effective methods based on semantic comprehension, whose traditional models focus on multiple rounds of detection to differentiate information. Since a large am...

With the rapid prevalence of mobile devices and the dramatic proliferation of mobile applications (apps), app recommendation becomes an emergent task that would benefit both app users and stockholders. How to effectively organize and make full use of rich side information of users and apps is a key challenge to address the sparsity issue for tradit...

We propose a heuristics-based social-sensor cloud service selection and composition model to reconstruct mosaic scenes. The proposed approach leverages crowdsourced social media images to create an image mosaic to reconstruct a scene at a designated location and an interval of time. The novel approach relies on the set of features defined on the ba...

This paper explores an effective machine learning approach to predict cloud market performance for cloud consumers, providers and investors based on social media. We identified a set of comprehensive subjective metrics that may affect cloud market performance via literature survey. We used a popular sentiment analysis technique to process customer...

This paper explores an effective machine learning approach to predict cloud market performance for cloud consumers, providers and investors based on social media. We identified a set of comprehensive subjective metrics that may affect cloud market performance via literature survey. We used a popular sentiment analysis technique to process customer...

We propose a heuristics-based social-sensor cloud service selection and composition model to reconstruct mosaic scenes. The proposed approach leverages crowdsourced social media images to create an image mosaic to reconstruct a scene at a designated location and an interval of time. The novel approach relies on the set of features defined on the ba...

With the rapid prevalence of mobile devices and the dramatic proliferation of mobile applications (apps), app recommendation becomes an emergent task that would benefit both app users and stockholders. How to effectively organize and make full use of rich side information of users and apps is a key challenge to address the sparsity issue for tradit...

Predictive analytics in Mobile Edge Computing (MEC) based Internet of Things (IoT) is becoming a high demand in many real-world applications. A prediction problem in an MEC-based IoT environment typically corresponds to a collection of tasks with each task solved in a specific MEC environment based on the data accumulated locally, which can be rega...

We propose a novel activity learning framework based on Edge Cloud architecture for the purpose of recognizing and predicting human activities. Although activity recognition has been vastly studied by many researchers, the temporal features that constitute an activity, which can provide useful insights for activity models, have not been exploited t...

The extensive use of social media platforms and overwhelming amounts of imagery data creates unique opportunities for sensing, gathering and sharing information about events. One of its potential applications is to leverage crowdsourced social media images to create a tapestry scene for scene analysis of designated locations and time intervals. The...

The extensive use of social media platforms and overwhelming amounts of imagery data creates unique opportunities for sensing, gathering and sharing information about events. One of its potential applications is to leverage crowdsourced social media images to create a tapestry scene for scene analysis of designated locations and time intervals. The...

Quality of Service (QoS) is well acknowledged as a decisive means for ascertaining the performance of third-party Web services. QoS has high uncertainty in complex and dynamic network environments. QoS monitoring is considered as one of the most effective techniques to detect QoS violations at runtime. However, existing QoS monitoring approaches on...

Existing cloud service selection techniques assume that service evaluation criteria are independent. In reality, there are different types of interactions between criteria. These interactions influence the performance of a service selection system in different ways. In addition, a lack of measurement indices to validate the performance of service s...