All Outputs (8)

UAVs and Blockchain Synergy: Enabling Secure Reputation-based Federated Learning in Smart Cities (2024)
Journal Article
Aqleem Abbas, S. M., Khan, M. A., Boulila, W., Kouba, A., Shahbaz Khan, M., & Ahmad, J. (online). UAVs and Blockchain Synergy: Enabling Secure Reputation-based Federated Learning in Smart Cities. IEEE Access, https://doi.org/10.1109/access.2024.3432610

Unmanned aerial vehicles (UAVs) can be used as drones’ edge Intelligence to assist with data collection, training models, and communication over wireless networks. UAV use for smart cities is rapidly growing in various industries, including tracking... Read More about UAVs and Blockchain Synergy: Enabling Secure Reputation-based Federated Learning in Smart Cities.

Chaotic Quantum Encryption to Secure Image Data in Post Quantum Consumer Technology (2024)
Journal Article
Khan, M. S., Ahmad, J., Al-Dubai, A., Pitropakis, N., Ghaleb, B., Ullah, A., Khan, M. A., & Buchanan, W. J. (online). Chaotic Quantum Encryption to Secure Image Data in Post Quantum Consumer Technology. IEEE Transactions on Consumer Electronics, https://doi.org/10.1109/tce.2024.3415411

The rapid advancement in consumer technology has led to an exponential increase in the connected devices, resulting in an enormous and continuous flow of data, particularly the image data. This data needs to be processed, managed, and secured efficie... Read More about Chaotic Quantum Encryption to Secure Image Data in Post Quantum Consumer Technology.

PermutEx: Feature-Extraction-Based Permutation — A New Diffusion Scheme for Image Encryption Algorithms (2024)
Presentation / Conference Contribution
Khan, M. S., Ahmad, J., Al-Dubai, A., Jaroucheh, Z., Pitropakis, N., & Buchanan, W. J. (2023, November). PermutEx: Feature-Extraction-Based Permutation — A New Diffusion Scheme for Image Encryption Algorithms. Presented at 2023 IEEE 28th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), Edinburgh, United Kingdom

Traditional permutation schemes mostly focus on random scrambling of pixels, often neglecting the intrinsic image information that could enhance diffusion in image encryption algorithms. This paper introduces PermutEx, a feature-extractionbased permu... Read More about PermutEx: Feature-Extraction-Based Permutation — A New Diffusion Scheme for Image Encryption Algorithms.

A novel medical image data protection scheme for smart healthcare system (2024)
Journal Article
Rehman, M. U., Shafique, A., Khan, M. S., Driss, M., Boulila, W., Ghadi, Y. Y., Changalasetty, S. B., Alhaisoni, M., & Ahmad, J. (online). A novel medical image data protection scheme for smart healthcare system. CAAI Transactions on Intelligence Technology, https://doi.org/10.1049/cit2.12292

The Internet of Multimedia Things (IoMT) refers to a network of interconnected multimedia devices that communicate with each other over the Internet. Recently, smart healthcare has emerged as a significant application of the IoMT, particularly in the... Read More about A novel medical image data protection scheme for smart healthcare system.

SRSS: A New Chaos-Based Single-Round Single S-Box Image Encryption Scheme for Highly Auto-Correlated Data (2023)
Presentation / Conference Contribution
Shahbaz Khan, M., Ahmad, J., Ali, H., Pitropakis, N., Al-Dubai, A., Ghaleb, B., & Buchanan, W. J. (2023, October). SRSS: A New Chaos-Based Single-Round Single S-Box Image Encryption Scheme for Highly Auto-Correlated Data. Presented at 9th International Conference on Engineering and Emerging Technologies (IEEE ICEET 2023), Istanbul, Turkey

With the advent of digital communication, securing digital images during transmission and storage has become a critical concern. The traditional s-box substitution methods often fail to effectively conceal the information within highly auto-correlate... Read More about SRSS: A New Chaos-Based Single-Round Single S-Box Image Encryption Scheme for Highly Auto-Correlated Data.

Artificial intelligence-driven approach to identify and recommend the winner in a tied event in sports surveillance (2023)
Journal Article
Anwar, K., Zafar, A., Iqbal, A., Sohail, S. S., Hussain, A., Karaca, Y., Hijji, M., Saudagar, A. K. J., & Muhammad, K. (2023). Artificial intelligence-driven approach to identify and recommend the winner in a tied event in sports surveillance. Fractals, 31(10), Article 2340149. https://doi.org/10.1142/s0218348x23401497

The proliferation of fractal artificial intelligence (AI)-based decision-making has propelled advances in intelligent computing techniques. Fractal AI-driven decision-making approaches are used to solve a variety of real-world complex problems, espec... Read More about Artificial intelligence-driven approach to identify and recommend the winner in a tied event in sports surveillance.

CellSecure: Securing Image Data in Industrial Internet-of-Things via Cellular Automata and Chaos-Based Encryption (2023)
Presentation / Conference Contribution
Ali, H., Khan, M. S., Driss, M., Ahmad, J., Buchanan, W. J., & Pitropakis, N. (2023, October). CellSecure: Securing Image Data in Industrial Internet-of-Things via Cellular Automata and Chaos-Based Encryption. Presented at 2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall), Hong Kong, Hong Kong

In the era of Industrial IoT (IIoT) and Industry 4.0, ensuring secure data transmission has become a critical concern. Among other data types, images are widely transmitted and utilized across various IIoT applications, ranging from sensor-generated... Read More about CellSecure: Securing Image Data in Industrial Internet-of-Things via Cellular Automata and Chaos-Based Encryption.

FireXnet: an explainable AI-based tailored deep learning model for wildfire detection on resource-constrained devices (2023)
Journal Article
Ahmad, K., Khan, M. S., Ahmed, F., Driss, M., Boulila, W., Alazeb, A., Alsulami, M., Alshehri, M. S., Ghadi, Y. Y., & Ahmad, J. (2023). FireXnet: an explainable AI-based tailored deep learning model for wildfire detection on resource-constrained devices. Fire Ecology, 19, Article 54. https://doi.org/10.1186/s42408-023-00216-0

Background: Forests cover nearly one-third of the Earth’s land and are some of our most biodiverse ecosystems. Due to climate change, these essential habitats are endangered by increasing wildfires. Wildfires are not just a risk to the environment, b... Read More about FireXnet: an explainable AI-based tailored deep learning model for wildfire detection on resource-constrained devices.