ORCID Profile
0000-0003-0655-666X
Current Organisations
Swinburne University of Technology
,
Deakin University
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Information Systems Management | Information Systems | Computer Software | Computer System Security | Information Engineering and Theory | Information Systems Development Methodologies | Web Technologies (excl. Web Search) | Database Management
ANZSRC Socio-Economic Objective (SEO)
Application Tools and System Utilities | Application Software Packages (excl. Computer Games) | Computer Software and Services not elsewhere classified | Mobile Data Networks and Services | Internet Hosting Services (incl. Application Hosting Services) |
Related Links
Publications
First-priority relation graph-based malicious users detection in mobile social networks
Publisher: Springer International Publishing
Date: 2015
Demystifying the Evolution of Android Malware Variants
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Using epidemic betweenness to measure the influence of users in complex networks
Publisher: Elsevier BV
Date: 2017
Are the popular users always important for information dissemination in online social networks?
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2014
Modeling Propagation Dynamics of Social Network Worms
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2013
A Man-in-the-Middle Attack on 3G-WLAN Interworking
Publisher: IEEE
Date: 04-2010
DOI: 10.1109/CMC.2010.34
Identifying Diffusion Sources in Large Networks: A Community Structure Based Approach
Publisher: IEEE
Date: 08-2015
To Shut Them Up or to Clarify: Restraining the Spread of Rumors in Online Social Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2014
Software Vulnerability Detection Using Deep Neural Networks: A Survey
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2020
Missing Value Filling Based on the Collaboration of Cloud and Edge in Artificial Intelligence of Things
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2022
Vulnerability Detection in SIoT Applications: A Fuzzing Method on their Binaries
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2022
Pokémon GO in Melbourne CBD: A case study of the cyber-physical symbiotic social networks
Publisher: Elsevier BV
Date: 05-2018
SAM: Multi-turn Response Selection Based on Semantic Awareness Matching
Publisher: Association for Computing Machinery (ACM)
Date: 28-02-2023
DOI: 10.1145/3545570
Abstract: Multi-turn response selection is a key issue in retrieval-based chatbots and has attracted considerable attention in the NLP (Natural Language processing) field. So far, researchers have developed many solutions that can select appropriate responses for multi-turn conversations. However, these works are still suffering from the semantic mismatch problem when responses and context share similar words with different meanings. In this article, we propose a novel chatbot model based on Semantic Awareness Matching, called SAM. SAM can capture both similarity and semantic features in the context by a two-layer matching network. Appropriate responses are selected according to the matching probability made through the aggregation of the two feature types. In the evaluation, we pick 4 widely used datasets and compare SAM’s performance to that of 12 other models. Experiment results show that SAM achieves substantial improvements, with up to 1.5% R 10 @1 on Ubuntu Dialogue Corpus V2, 0.5% R 10 @1 on Douban Conversation Corpus, and 1.3% R 10 @1 on E-commerce Corpus.
Reliable wireless connections for fast-moving rail users based on a chained fog structure
Publisher: Elsevier BV
Date: 02-2017
Digging the Underlying Layers of Intelligent Transportation System Networks by Mobile Crowdsourcing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
A Sword with Two Edges: Propagation Studies on Both Positive and Negative Information in Online Social Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2015
Fuzzing With Optimized Grammar-Aware Mutation Strategies
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
On the Security of Networked Control Systems in Smart Vehicle and Its Adaptive Cruise Control
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
Backdoor Attack on Machine Learning Based Android Malware Detectors
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2022
Daedalus: Breaking Nonmaximum Suppression in Object Detection via Adversarial Examples
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2022
Detecting stepping stones by abnormal causality probability
Publisher: Wiley
Date: 23-06-2014
DOI: 10.1002/SEC.1037
CALD: Surviving Various Application-Layer DDoS Attacks That Mimic Flash Crowd
Publisher: IEEE
Date: 09-2010
DOI: 10.1109/NSS.2010.69
Synthesized Corpora to Evaluate Fuzzing for Green Internet of Things Programs
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
The Microcosmic Model of Worm Propagation
Publisher: Oxford University Press (OUP)
Date: 19-08-2011
Bandwidth-aware energy efficient flow scheduling with SDN in data center networks
Publisher: Elsevier BV
Date: 03-2017
Traceable Identity-Based Group Signature
Publisher: EDP Sciences
Date: 07-2016
DOI: 10.1051/ITA/2016021
TouchWB: Touch behavioral user authentication based on web browsing on smartphones
Publisher: Elsevier BV
Date: 09-2018
Detection and defense of application-layer DDoS attacks in backbone web traffic
Publisher: Elsevier BV
Date: 09-2014
Modeling and Analysis on the Propagation Dynamics of Modern Email Malware
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2014
DOI: 10.1109/TDSC.2013.49
Eliminating Errors in Worm Propagation Models
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2011
Investigating the deceptive information in Twitter spam
Publisher: Elsevier BV
Date: 07-2017
Efficient and secure attribute-based signature for monotone predicates
Publisher: Springer Science and Business Media LLC
Date: 23-05-2016
Catch Me if You Can: Detecting Compromised Users Through Partial Observation on Networks
Publisher: IEEE
Date: 06-2017
On-street car parking prediction in smart city: A multi-source data analysis in sensor-cloud environment
Publisher: Springer International Publishing
Date: 2017
Maximizing real-time streaming services based on a multi-servers networking framework
Publisher: Elsevier BV
Date: 12-2015
Space-Efficient Storage Structure of Blockchain Transactions Supporting Secure Verification
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Real-Time Detection of COVID-19 Events From Twitter: A Spatial-Temporally Bursty-Aware Method
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2023
Rumor Source Identification in Social Networks with Time-Varying Topology
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Personalized Location Privacy Protection for Location-Based Services in Vehicular Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Social Media Event Prediction using DNN with Feedback Mechanism
Publisher: Association for Computing Machinery (ACM)
Date: 14-05-2022
DOI: 10.1145/3522759
Abstract: Online social networks (OSNs) are a rich source of information, and the data (including user-generated content) can be mined to facilitate real-world event prediction. However, the dynamic nature of OSNs and the fast-pace nature of social events or hot topics compound the challenge of event prediction. This is a key limitation in many existing approaches. For ex le, our evaluations of six baseline approaches (i.e., logistic regression latent Dirichlet allocation (LDA)-based logistic regression (LR), multi-task learning (MTL), long short-term memory (LSTM) and convolutional neural networks, and transformer-based model) on three datasets collected as part of this research (two from Twitter and one from a news collection site 1 ), reveal that the accuracy of these approaches is between 50% and 60%, and they are not capable of utilizing new events in event predictions. Hence, in this article, we develop a novel DNN-based framework (hereafter referred to as event prediction with feedback mechanism— EPFM . Specifically, EPFM makes use of a feedback mechanism based on emerging events detection to improve the performance of event prediction. The feedback mechanism ensembles three outlier detection processes and returns a list of new events. Some of the events will then be chosen by analysts to feed into the fine-tuning process to update the predictive model. To evaluate EPFM, we conduct a series of experiments on the same three datasets, whose findings show that EPFM achieves 80% accuracy in event detection and outperforms the six baseline approaches.We also validate EPFM’s capability of detecting new events by empirically analyzing the feedback mechanism under different thresholds.
Mobility increases the risk of malware propagations in wireless networks
Publisher: IEEE
Date: 08-2015
Following targets for mobile tracking in wireless sensor networks
Publisher: Association for Computing Machinery (ACM)
Date: 07-09-2016
DOI: 10.1145/2968450
Abstract: Traditional tracking solutions in wireless sensor networks based on fixed sensors have several critical problems. First, due to the mobility of targets, a lot of sensors have to keep being active to track targets in all potential directions, which causes excessive energy consumption. Second, when there are holes in the deployment area, targets may fail to be detected when moving into holes. Third, when targets stay at certain positions for a long time, sensors surrounding them have to suffer heavier work pressure than do others, which leads to a bottleneck for the entire network. To solve these problems, a few mobile sensors are introduced to follow targets directly for tracking because the energy capacity of mobile sensors is less constrained and they can detect targets closely with high tracking quality. Based on a realistic detection model, a solution of scheduling mobile sensors and fixed sensors for target tracking is proposed. Moreover, the movement path of mobile sensors has a provable performance bound compared to the optimal solution. Results of extensive simulations show that mobile sensors can improve tracking quality even if holes exist in the area and can reduce energy consumption of sensors effectively.
Twitter spam detection: Survey of new approaches and comparative study
Publisher: Elsevier BV
Date: 07-2018
Propagation modeling and defending of a mobile sensor worm in wireless sensor and actuator networks
Publisher: MDPI AG
Date: 13-01-2017
DOI: 10.3390/S17010139
Locating Defense Positions for Thwarting the Propagation of Topological Worms
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2012
Addictive Incentive Mechanism in Crowdsensing From the Perspective of Behavioral Economics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2022
Detecting spamming activities in twitter based on deep‐learning technique
Publisher: Wiley
Date: 20-06-2017
DOI: 10.1002/CPE.4209
On the Race of Worms and Patches: Modeling the Spread of Information in Wireless Sensor Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2016
How Spam Features Change in Twitter and the Impact to Machine Learning Based Detection
Publisher: Springer International Publishing
Date: 2017
Federated Learning in Industrial IoT: A Privacy-Preserving Solution That Enables Sharing of Data in Hydrocarbon Explorations
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Detecting Stepping Stones by Abnormal Causality Probability
Publisher: Springer International Publishing
Date: 2013
The Fog computing paradigm: Scenarios and security issues
Publisher: IEEE
Date: 29-09-2014
DOI: 10.15439/2014F503
Intersection Traffic Prediction Using Decision Tree Models
Publisher: MDPI AG
Date: 07-09-2018
DOI: 10.3390/SYM10090386
Abstract: Traffic prediction is a critical task for intelligent transportation systems (ITS). Prediction at intersections is challenging as it involves various participants, such as vehicles, cyclists, and pedestrians. In this paper, we propose a novel approach for the accurate intersection traffic prediction by introducing extra data sources other than road traffic volume data into the prediction model. In particular, we take advantage of the data collected from the reports of road accidents and roadworks happening near the intersections. In addition, we investigate two types of learning schemes, namely batch learning and online learning. Three popular ensemble decision tree models are used in the batch learning scheme, including Gradient Boosting Regression Trees (GBRT), Random Forest (RF) and Extreme Gradient Boosting Trees (XGBoost), while the Fast Incremental Model Trees with Drift Detection (FIMT-DD) model is adopted for the online learning scheme. The proposed approach is evaluated using public data sets released by the Victorian Government of Australia. The results indicate that the accuracy of intersection traffic prediction can be improved by incorporating nearby accidents and roadworks information.
The structure of communities in scale‐free networks
Publisher: Wiley
Date: 04-11-2016
DOI: 10.1002/CPE.4040
An Analytical Model on the Propagation of Modern Email Worms
Publisher: IEEE
Date: 1
My face is mine: Fighting unpermitted tagging on personal/group photos in social media
Publisher: Springer International Publishing
Date: 2017
K-Center: An Approach on the Multi-Source Identification of Information Diffusion
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2015
Secure fine-grained spatio-temporal Top-k
Publisher: Elsevier BV
Date: 09-2018
An overview of Fog computing and its security issues
Publisher: Wiley
Date: 29-04-2016
DOI: 10.1002/CPE.3485
Analysis of the Spreading Influence Variations for Online Social Users under Attacks
Publisher: IEEE
Date: 12-2016
How Does Visualisation Help App Practitioners Analyse Android Apps?
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2023
Modeling and Analysis for Thwarting Worm Propagation in Email Networks
Publisher: Springer Berlin Heidelberg
Date: 2013
The Probability Model of Peer-to-Peer Botnet Propagation
Publisher: Springer Berlin Heidelberg
Date: 2011
CAFS: a novel lightweight cache‐based scheme for large‐scale intrusion alert fusion
Publisher: Wiley
Date: 28-04-2011
DOI: 10.1002/CPE.1747
Modeling worms propagation on probability
Publisher: IEEE
Date: 09-2011
Defending Against Adversarial Attack Towards Deep Neural Networks Via Collaborative Multi-Task Training
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2022
Covert attacks through adversarial learning
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
A Privacy-Preserving Approach to Identify COVID-19 Infection Origins via Volunteered Share of Health Data Records by Mobile Users
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-01-2023
The Relation Between Local and Global Influence of Individuals in Scale-Free Networks
Publisher: IEEE
Date: 11-2015
Identifying Propagation Sources in Networks: State-of-the-Art and Comparative Studies
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Modeling the Propagation of Worms in Networks: A Survey
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2014
A Lightweight Intrusion Alert Fusion System
Publisher: IEEE
Date: 09-2010
Fuzzing: A Survey for Roadmap
Publisher: Association for Computing Machinery (ACM)
Date: 31-01-2022
DOI: 10.1145/3512345
Abstract: Fuzz testing (fuzzing) has witnessed its prosperity in detecting security flaws recently. It generates a large number of test cases and monitors the executions for defects. Fuzzing has detected thousands of bugs and vulnerabilities in various applications. Although effective, there lacks systematic analysis of gaps faced by fuzzing. As a technique of defect detection, fuzzing is required to narrow down the gaps between the entire input space and the defect space. Without limitation on the generated inputs, the input space is infinite. However, defects are sparse in an application, which indicates that the defect space is much smaller than the entire input space. Besides, because fuzzing generates numerous test cases to repeatedly examine targets, it requires fuzzing to perform in an automatic manner. Due to the complexity of applications and defects, it is challenging to automatize the execution of erse applications. In this article, we systematically review and analyze the gaps as well as their solutions, considering both breadth and depth. This survey can be a roadmap for both beginners and advanced developers to better understand fuzzing.
Dynalogue: A Transformer-Based Dialogue System with Dynamic Attention
Publisher: ACM
Date: 30-04-2023
Related Organisations
Related Funding Activities
Linkage Projects - Grant ID: LP170100924
Start Date: 01-2019
End Date: 01-2023
Amount: $450,000.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP220102784
Start Date: 2022
End Date: 12-2025
Amount: $450,000.00
Funder: Australian Research Council
View Funded ActivityLinkage Projects - Grant ID: LP180100170
Start Date: 07-2019
End Date: 06-2024
Amount: $360,000.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP200100886
Start Date: 2020
End Date: 12-2024
Amount: $420,000.00
Funder: Australian Research Council
View Funded ActivityLinkage Projects - Grant ID: LP200200084
Start Date: 10-2021
End Date: 10-2024
Amount: $407,839.00
Funder: Australian Research Council
View Funded Activity