Outputs (701)

A New Mobility Aware Duty Cycling and Dynamic Preambling Algorithm for Wireless Sensor Network (2020)
Presentation / Conference Contribution
Thomson, C., Wadhaj, I., Al-Dubai, A., & Tan, Z. (2020, April). A New Mobility Aware Duty Cycling and Dynamic Preambling Algorithm for Wireless Sensor Network. Presented at IEEE 6th World Forum on Internet of Things, New Orleans, Louisiana, USA

The issue of energy holes, or hotspots, in wireless sensor networks is well referenced. As is the proposed mobilisa-tion of the sink node in order to combat this. However, as the sink node shall still pass some nodes more closely and frequently than... Read More about A New Mobility Aware Duty Cycling and Dynamic Preambling Algorithm for Wireless Sensor Network.

Deep Learning and Dempster-Shafer Theory Based Insider Threat Detection (2020)
Journal Article
Tian, Z., Shi, W., Tan, Z., Qiu, J., Sun, Y., Jiang, F., & Liu, Y. (online). Deep Learning and Dempster-Shafer Theory Based Insider Threat Detection. Mobile Networks and Applications, https://doi.org/10.1007/s11036-020-01656-7

Organizations' own personnel now have a greater ability than ever before to misuse their access to critical organizational assets. Insider threat detection is a key component in identifying rare anomalies in context, which is a growing concern for ma... Read More about Deep Learning and Dempster-Shafer Theory Based Insider Threat Detection.

A Novel Cost Optimization Strategy for SDN-Enabled UAV-Assisted Vehicular Computation Offloading (2020)
Journal Article
Zhao, L., Yang, K., Tan, Z., Li, X., Sharma, S., & Liu, Z. (2021). A Novel Cost Optimization Strategy for SDN-Enabled UAV-Assisted Vehicular Computation Offloading. IEEE Transactions on Intelligent Transportation Systems, 22(6), 3664-3674. https://doi.org/10.1109/TITS.2020.3024186

Vehicular computation offloading is a well-received strategy to execute delay-sensitive and/or compute-intensive tasks of legacy vehicles. The response time of vehicular computation offloading can be shortened by using mobile edge computing that offe... Read More about A Novel Cost Optimization Strategy for SDN-Enabled UAV-Assisted Vehicular Computation Offloading.

BeepTrace: Blockchain-enabled Privacy-preserving Contact Tracing for COVID-19 Pandemic and Beyond (2020)
Journal Article
Xu, H., Zhang, L., Onireti, O., Fang, Y., Buchanan, W. J., & Imran, M. A. (2021). BeepTrace: Blockchain-enabled Privacy-preserving Contact Tracing for COVID-19 Pandemic and Beyond. IEEE Internet of Things, 8(5), 3915-3929. https://doi.org/10.1109/jiot.2020.3025953

The outbreak of COVID-19 pandemic has exposed an urgent need for effective contact tracing solutions through mobile phone applications to prevent the infection from spreading further. However, due to the nature of contact tracing, public concern on p... Read More about BeepTrace: Blockchain-enabled Privacy-preserving Contact Tracing for COVID-19 Pandemic and Beyond.

Novel Architecture and Heuristic Algorithms for Software-Defined Wireless Sensor Networks (2020)
Journal Article
Hawbani, A., Wang, X., Zhao, L., Al-Dubai, A., Min, G., & Busaileh, O. (2020). Novel Architecture and Heuristic Algorithms for Software-Defined Wireless Sensor Networks. IEEE/ACM Transactions on Networking, 28(6), 2809-2822. https://doi.org/10.1109/TNET.2020.3020984

This article extends the promising software-defined networking technology to wireless sensor networks to achieve two goals: 1) reducing the information exchange between the control and data planes, and 2) counterbalancing between the sender's waiting... Read More about Novel Architecture and Heuristic Algorithms for Software-Defined Wireless Sensor Networks.

A Distributed Trust Framework for Privacy-Preserving Machine Learning (2020)
Presentation / Conference Contribution
Abramson, W., Hall, A. J., Papadopoulos, P., Pitropakis, N., & Buchanan, W. J. (2020, September). A Distributed Trust Framework for Privacy-Preserving Machine Learning. Presented at The 17th International Conference on Trust, Privacy and Security in Digital Business - TrustBus2020, Bratislava, Slovakia

When training a machine learning model, it is standard procedure for the researcher to have full knowledge of both the data and model. However, this engenders a lack of trust between data owners and data scientists. Data owners are justifiably reluct... Read More about A Distributed Trust Framework for Privacy-Preserving Machine Learning.

FPC-BI: Fast Probabilistic Consensus within Byzantine Infrastructures (2020)
Journal Article
Popov, S., & Buchanan, W. J. (2021). FPC-BI: Fast Probabilistic Consensus within Byzantine Infrastructures. Journal of Parallel and Distributed Computing, 147, 77-86. https://doi.org/10.1016/j.jpdc.2020.09.002

This paper presents a novel leaderless protocol (FPC-BI: Fast Probabilistic Consensus within Byzantine Infrastructures) with a low communicational complexity and which allows a set of nodes to come to a consensus on a value of a single bit. The paper... Read More about FPC-BI: Fast Probabilistic Consensus within Byzantine Infrastructures.

Machine Learning-driven Optimization for Intrusion Detection in Smart Vehicular Networks (2020)
Journal Article
Alsarhan, A., Al-Ghuwairi, A.-R., Almalkaw, I., Alauthman, M., & Al-Dubai, A. (2021). Machine Learning-driven Optimization for Intrusion Detection in Smart Vehicular Networks. Wireless Personal Communications, 117, 3129-3152 (2021). https://doi.org/10.1007/s11277-020-07797-y

An essential element in the smart city vision is providing safe and secure journeys via intelligent vehicles and smart roads. Vehicular ad hoc networks (VANETs) have played a significant role in enhancing road safety where vehicles can share road inf... Read More about Machine Learning-driven Optimization for Intrusion Detection in Smart Vehicular Networks.

Tuft: Tree Based Heuristic Data Dissemination for Mobile Sink Wireless Sensor Networks (2020)
Journal Article
Busaileh, O., Hawbani, A., Xingfu, W., Liu, P., Zhao, L., & Al-Dubai, A. (2022). Tuft: Tree Based Heuristic Data Dissemination for Mobile Sink Wireless Sensor Networks. IEEE Transactions on Mobile Computing, 21(4), 1520-1536. https://doi.org/10.1109/TMC.2020.3022403

Wireless sensor networks (WSNs) with a static sink suffer from concentrated data traffic in the vicinity of the sink, which increases the burden on the nodes surrounding the sink, and impels them to deplete their batteries faster than other nodes in... Read More about Tuft: Tree Based Heuristic Data Dissemination for Mobile Sink Wireless Sensor Networks.

Improving Classification of Metamorphic Malware by Augmenting Training Data with a Diverse Set of Evolved Mutant Samples (2020)
Presentation / Conference Contribution
Babaagba, K., Tan, Z., & Hart, E. (2020, July). Improving Classification of Metamorphic Malware by Augmenting Training Data with a Diverse Set of Evolved Mutant Samples. Presented at The 2020 IEEE Congress on Evolutionary Computation (IEEE CEC 2020), Glasgow, UK

Detecting metamorphic malware provides a challenge to machine-learning models as trained models might not generalise to future mutant variants of the malware. To address this, we explore whether machine-learning models can be improved by augmenting t... Read More about Improving Classification of Metamorphic Malware by Augmenting Training Data with a Diverse Set of Evolved Mutant Samples.

DNA and Plaintext Dependent Chaotic Visual Selective Image Encryption (2020)
Journal Article
Khan, J. S., Boulila, W., Ahmad, J., Rubaiee, S., Rehman, A. U., Alroobaea, R., & Buchanan, W. J. (2020). DNA and Plaintext Dependent Chaotic Visual Selective Image Encryption. IEEE Access, 8, 159732-159744. https://doi.org/10.1109/access.2020.3020917

Visual selective image encryption can both improve the efficiency of the image encryption algorithm and reduce the frequency and severity of attacks against data. In this article, a new form of encryption is proposed based on keys derived from Deoxyri... Read More about DNA and Plaintext Dependent Chaotic Visual Selective Image Encryption.

Evaluation of Ensemble Learning for Android Malware Family Identification (2020)
Journal Article
Wylie, J., Tan, Z., Al-Dubai, A., & Wang, J. (2020). Evaluation of Ensemble Learning for Android Malware Family Identification. Journal of Guangzhou University (Natural Science Edition), 19(4), 28-41

Every Android malware sample generally belongs to a specific family that performs a similar set of actions and characteristics. Having the ability to effectively identify Android malware families can assist in addressing the damage caused by malware.... Read More about Evaluation of Ensemble Learning for Android Malware Family Identification.

Block-Sparse Coding-Based Machine Learning Approach for Dependable Device-Free Localization in IoT Environment (2020)
Journal Article
Zhao, L., Huang, H., Su, C., Ding, S., Huang, H., Tan, Z., & Li, Z. (2021). Block-Sparse Coding-Based Machine Learning Approach for Dependable Device-Free Localization in IoT Environment. IEEE Internet of Things Journal, 8(5), 3211-3223. https://doi.org/10.1109/jiot.2020.3019732

Device-free localization (DFL) locates targets without equipping with wireless devices or tag under the Internet-of-Things (IoT) architectures. As an emerging technology, DFL has spawned extensive applications in IoT environment, such as intrusion de... Read More about Block-Sparse Coding-Based Machine Learning Approach for Dependable Device-Free Localization in IoT Environment.

FPDP: Flexible Privacy-preserving Data Publishing Scheme for Smart Agriculture (2020)
Journal Article
Song, J., Zhong, Q., Su, C., Tan, Z., & Liu, Y. (2021). FPDP: Flexible Privacy-preserving Data Publishing Scheme for Smart Agriculture. IEEE Sensors Journal, 21(16), 17430-17438. https://doi.org/10.1109/JSEN.2020.3017695

Food security is a global concern. Benefit from the development of 5G, IoT is used in agriculture to help the farmers to maintain and improve productivity. It not only enables the customers, both at home and abroad, to become more informed about the... Read More about FPDP: Flexible Privacy-preserving Data Publishing Scheme for Smart Agriculture.

Trust-based Ecosystem to Combat Fake News (2020)
Presentation / Conference Contribution
Jaroucheh, Z., Alissa, M., & Buchanan, W. J. (2020, May). Trust-based Ecosystem to Combat Fake News. Presented at 2020 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), Toronto, ON, Canada

The growing spread of misinformation and dis-information has grave political, social, ethical, and privacy implications for society. Therefore, there is an ethical need to combat the flow of fake news. This paper attempts to resolves some of the aspe... Read More about Trust-based Ecosystem to Combat Fake News.

IEEE Access Special Section Editorial: Security and Trusted Computing for Industrial Internet of Things: Research Challenges and Opportunities (2020)
Journal Article
Li, S., Choo, K.-K. R., Tan, Z., He, X., Hu, J., & Qin, T. (2020). IEEE Access Special Section Editorial: Security and Trusted Computing for Industrial Internet of Things: Research Challenges and Opportunities. IEEE Access, 8, 145033-145036. https://doi.org/10.1109/access.2020.3014416

Industrial IoT (IIoT) interconnects critical devices and sensors in critical infrastructure sectors with existing Internet of Things (IoT) devices and applications. Generally, IIoT deployment allows organizations and users to gain invaluable insights... Read More about IEEE Access Special Section Editorial: Security and Trusted Computing for Industrial Internet of Things: Research Challenges and Opportunities.

Privacy-Preserving Passive DNS (2020)
Journal Article
Papadopoulos, P., Pitropakis, N., Buchanan, W. J., Lo, O., & Katsikas, S. (2020). Privacy-Preserving Passive DNS. Computers, 9(3), Article 64. https://doi.org/10.3390/computers9030064

The Domain Name System (DNS) was created to resolve the IP addresses of web servers to easily remembered names. When it was initially created, security was not a major concern; nowadays, this lack of inherent security and trust has exposed the global... Read More about Privacy-Preserving Passive DNS.

Chaos-Based Confusion and Diffusion of Image Pixels Using Dynamic Substitution (2020)
Journal Article
Qayyum, A., Ahmad, J., Boulila, W., Rubaiee, S., Arshad, Masood, F., Khan, F., & Buchanan, W. J. (2020). Chaos-Based Confusion and Diffusion of Image Pixels Using Dynamic Substitution. IEEE Access, 8, 140876-140895. https://doi.org/10.1109/access.2020.3012912

The evolution of wireless and mobile communication from 0G to the upcoming 5G gives riseto data sharing through the Internet. This data transfer via open public networks are susceptible to severaltypes of attacks. Encryption is a method that can prot... Read More about Chaos-Based Confusion and Diffusion of Image Pixels Using Dynamic Substitution.

An Ontological Model for Courses and Academic Profiles Representation: A case study of King Abdulaziz University (2020)
Presentation / Conference Contribution
Ashour, G., Al-Dubai, A., Romdhani, I., & Aljohani, N. (2020, June). An Ontological Model for Courses and Academic Profiles Representation: A case study of King Abdulaziz University. Presented at 2020 International Conference Engineering Technologies and Computer Science (EnT), Moscow, Russia

Educational data is growing continuously. This huge amount of data that is scattered and come from different resources with different formats usually is noisy, duplicated, inconsistent, and unorganized. These data can be more efficient and usable whe... Read More about An Ontological Model for Courses and Academic Profiles Representation: A case study of King Abdulaziz University.

Towards Identifying Human Actions, Intent, and Severity of APT Attacks Applying Deception Techniques - An Experiment (2020)
Presentation / Conference Contribution
Chacon, J., Mckeown, S., & Macfarlane, R. (2020, June). Towards Identifying Human Actions, Intent, and Severity of APT Attacks Applying Deception Techniques - An Experiment. Presented at IEEE International Conference on Cyber Security and Protection of Digital Services (Cyber Security 2020), Dublin, Ireland

Attacks by Advanced Persistent Threats (APTs) have been shown to be difficult to detect using traditional signature-and anomaly-based intrusion detection approaches. Deception techniques such as decoy objects, often called honey items, may be deploye... Read More about Towards Identifying Human Actions, Intent, and Severity of APT Attacks Applying Deception Techniques - An Experiment.