Outputs (31)

A novel transformer-based explainable AI approach using SHAP for intrusion detection in vehicular ad hoc networks (2025)
Journal Article
Khan, W., Ahmad, J., Alasbali, N., Mazroa, A. A., Alshehri, M. S., & Khan, M. S. (2025). A novel transformer-based explainable AI approach using SHAP for intrusion detection in vehicular ad hoc networks. Computer Networks, 270, Article 111575. https://doi.org/10.1016/j.comnet.2025.111575

Vehicular ad hoc networks (VANETs)— a technology to connect autonomous vehicles to enhance the safety and decision-making on the road by enabling wireless communication and sharing of traffic information between sensors, vehicles, and other infrastru... Read More about A novel transformer-based explainable AI approach using SHAP for intrusion detection in vehicular ad hoc networks.

An explainable and efficient deep learning framework for EEG-based diagnosis of Alzheimer's disease and frontotemporal dementia (2025)
Journal Article
Khan, W., Khan, M. S., Qasem, S. N., Ghaban, W., Saeed, F., Hanif, M., & Ahmad, J. (2025). An explainable and efficient deep learning framework for EEG-based diagnosis of Alzheimer's disease and frontotemporal dementia. Frontiers in Medicine, 12, Article 1590201. https://doi.org/10.3389/fmed.2025.1590201

The early and accurate diagnosis of Alzheimer's Disease and Frontotemporal Dementia remains a critical challenge, particularly with traditional machine learning models which often fail to provide transparency in their predictions, reducing user confi... Read More about An explainable and efficient deep learning framework for EEG-based diagnosis of Alzheimer's disease and frontotemporal dementia.

OculusNet: Detection of retinal diseases using a tailored web-deployed neural network and saliency maps for explainable AI (2025)
Journal Article
Umair, M., Ahmad, J., Saidani, O., Alshehri, M. S., Al Mazroa, A., Hanif, M., Ullah, R., & Khan, M. S. (2025). OculusNet: Detection of retinal diseases using a tailored web-deployed neural network and saliency maps for explainable AI. Frontiers in Medicine, 12, Article 1596726. https://doi.org/10.3389/fmed.2025.1596726

Retinal diseases are among the leading causes of blindness worldwide, requiring early detection for effective treatment. Manual interpretation of ophthalmic imaging, such as optical coherence tomography (OCT), is traditionally time-consuming, prone t... Read More about OculusNet: Detection of retinal diseases using a tailored web-deployed neural network and saliency maps for explainable AI.

TrustShare: Secure and Trusted Blockchain Framework for Threat Intelligence Sharing (2025)
Journal Article
Ali, H., Buchanan, W. J., Ahmad, J., Abubakar, M., Khan, M. S., & Wadhaj, I. (2025). TrustShare: Secure and Trusted Blockchain Framework for Threat Intelligence Sharing. Future Internet, 17(7), Article 289. https://doi.org/10.3390/fi17070289

We introduce TrustShare, a novel blockchain-based framework designed to enable secure, privacy-preserving, and trust-aware cyber threat intelligence (CTI) sharing across organizational boundaries. Leveraging Hyperledger Fabric, the architecture suppo... Read More about TrustShare: Secure and Trusted Blockchain Framework for Threat Intelligence Sharing.

Enhancing Security in DNP3 Communication for Smart Grids: A Segmented Neural Network Approach (2025)
Journal Article
Bakhsh, S. A., Khan, M. S., Saidani, O., Alasbali, N., Abbas, S. Q., Khan, M. A., & Ahmad, J. (2025). Enhancing Security in DNP3 Communication for Smart Grids: A Segmented Neural Network Approach. IEEE Access, 13, 110436-110456. https://doi.org/10.1109/access.2025.3580507

The Distributed Network Protocol 3 (DNP3) protocols focus on securing critical infrastructure communication in sectors such as energy and supervisory control and data acquisition (SCADA) systems. The security of DNP3 is paramount, employing features... Read More about Enhancing Security in DNP3 Communication for Smart Grids: A Segmented Neural Network Approach.

Enhancing security in 6G-enabled wireless sensor networks for smart cities: a multi-deep learning intrusion detection approach (2025)
Journal Article
Khan, W., Usama, M., Khan, M. S., Saidani, O., Al Hamadi, H., Alnazzawi, N., Alshehri, M. S., & Ahmad, J. (2025). Enhancing security in 6G-enabled wireless sensor networks for smart cities: a multi-deep learning intrusion detection approach. Frontiers in Sustainable Cities, 7, Article 1580006. https://doi.org/10.3389/frsc.2025.1580006

Introduction: Wireless Sensor Networks (WSNs) play a critical role in the development of sustainable and intelligent smart city infrastructures, enabling data-driven services such as smart mobility, environmental monitoring, and public safety. As the... Read More about Enhancing security in 6G-enabled wireless sensor networks for smart cities: a multi-deep learning intrusion detection approach.

Decentralized EEG-based detection of major depressive disorder via transformer architectures and split learning (2025)
Journal Article
Umair, M., Ahmad, J., Alasbali, N., Saidani, O., Hanif, M., Khattak, A. A., & Khan, M. S. (2025). Decentralized EEG-based detection of major depressive disorder via transformer architectures and split learning. Frontiers in Computational Neuroscience, 19, Article 1569828. https://doi.org/10.3389/fncom.2025.1569828

Introduction: Major Depressive Disorder (MDD) remains a critical mental health concern, necessitating accurate detection. Traditional approaches to diagnosing MDD often rely on manual Electroencephalography (EEG) analysis to identify potential disord... Read More about Decentralized EEG-based detection of major depressive disorder via transformer architectures and split learning.

Privacy-enhanced skin disease classification: integrating federated learning in an IoT-enabled edge computing (2025)
Journal Article
Alasbali, N., Ahmad, J., Siddique, A. A., Saidani, O., Al Mazroa, A., Raza, A., Ullah, R., & Khan, M. S. (2025). Privacy-enhanced skin disease classification: integrating federated learning in an IoT-enabled edge computing. Frontiers in Computer Science, 7, Article 1550677. https://doi.org/10.3389/fcomp.2025.1550677

Introduction: The accurate and timely diagnosis of skin diseases is a critical concern, as many skin diseases exhibit similar symptoms in the early stages. Most existing automated detection/classification approaches that utilize machine learning or d... Read More about Privacy-enhanced skin disease classification: integrating federated learning in an IoT-enabled edge computing.

Enhancing AI-Generated Image Detection with a Novel Approach and Comparative Analysis (2025)
Presentation / Conference Contribution
Weir, S., Khan, M. S., Moradpoor, N., & Ahmad, J. (2024, December). Enhancing AI-Generated Image Detection with a Novel Approach and Comparative Analysis. Presented at 2024 17th International Conference on Security of Information and Networks (SIN), Sydney, Australia

This study explores advancements in AI-generated image detection, emphasizing the increasing realism of images, including deepfakes, and the need for effective detection methods. Traditional Convolutional Neural Networks (CNNs) have shown success but... Read More about Enhancing AI-Generated Image Detection with a Novel Approach and Comparative Analysis.

A Novel Cosine-Modulated-Polynomial Chaotic Map to Strengthen Image Encryption Algorithms in IoT Environments (2024)
Presentation / Conference Contribution
Khan, M. S., Ahmad, J., Al-Dubai, A., Pitropakis, N., Driss, M., & Buchanan, W. J. (2024, September). A Novel Cosine-Modulated-Polynomial Chaotic Map to Strengthen Image Encryption Algorithms in IoT Environments. Presented at 28th International Conference on Knowledge Based and Intelligent information and Engineering Systems (KES 2024), Spain

With the widespread use of the Internet of Things (IoT), securing the storage and transmission of multimedia content across IoT devices is a critical concern. Chaos-based Pseudo-Random Number Generators (PRNGs) play an essential role in enhancing the... Read More about A Novel Cosine-Modulated-Polynomial Chaotic Map to Strengthen Image Encryption Algorithms in IoT Environments.