Muhammad Shahbaz Khan's Outputs (21)

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. (2024). Chaotic Quantum Encryption to Secure Image Data in Post Quantum Consumer Technology. IEEE Transactions on Consumer Electronics, 70(4), 7087 - 7101. 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.

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.

A DNA Based Colour Image Encryption Scheme Using A Convolutional Autoencoder (2023)
Journal Article
Ahmed, F., Rehman, M. U., Ahmad, J., Khan, M. S., Boulila, W., Srivastava, G., Lin, J. C.-W., & Buchanan, W. J. (2023). A DNA Based Colour Image Encryption Scheme Using A Convolutional Autoencoder. ACM transactions on multimedia computing communications and applications, 19(3s), Article 128. https://doi.org/10.1145/3570165

With the advancement in technology, digital images can easily be transmitted and stored over the Internet. Encryption is used to avoid illegal interception of digital images. Encrypting large-sized colour images in their original dimension generally... Read More about A DNA Based Colour Image Encryption Scheme Using A Convolutional Autoencoder.

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. (2024). A novel medical image data protection scheme for smart healthcare system. CAAI Transactions on Intelligence Technology, 9(4), 821-836. 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.

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.

Enhancing AI-Generated Image Detection with a Novel Approach and Comparative Analysis (2024)
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. Paper presented at SINCONF 2024: 17th International Conference on Security of Information and Networks (SIN'24), Online

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.

VisCrypt: Image Encryption Featuring Novel Chaotic Key Generation and Block Permutation Techniques with Visual Cryptography (2024)
Presentation / Conference Contribution
Khan, M. S., Ahmad, J., Ali, M., Al Dubai, A., Pitropakis, N., & Buchanan, W. J. (2024, July). VisCrypt: Image Encryption Featuring Novel Chaotic Key Generation and Block Permutation Techniques with Visual Cryptography. Presented at 2024 IEEE 7th International Conference on Advanced Technologies, Signal and Image Processing (ATSIP), Sousse, Tunisia

In this digital era, ensuring the security of data transmission is critically important. Digital data, especially image data, needs to be secured against unauthorized access. In this regards, this paper presents a robust image encryption scheme named... Read More about VisCrypt: Image Encryption Featuring Novel Chaotic Key Generation and Block Permutation Techniques with Visual Cryptography.

Image Encryption Using A Novel Orbital-Extraction Permutation Technique and Chaotic Key Generation (2024)
Presentation / Conference Contribution
Khan, S., Ullah, S., Ahmad, J., Ullah, A., Arshad, A., & Khan, M. S. (2024, July). Image Encryption Using A Novel Orbital-Extraction Permutation Technique and Chaotic Key Generation. Presented at 2024 IEEE 7th International Conference on Advanced Technologies, Signal and Image Processing (ATSIP), Sousse, Tunisia

Abstract:
This paper presents an image encryption scheme that introduces a novel permutation technique named as orbital-extraction permutation. The proposed encryption scheme contains three important modules, i.e., the key generation module, the or... Read More about Image Encryption Using A Novel Orbital-Extraction Permutation Technique and Chaotic Key Generation.

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. (2024). UAVs and Blockchain Synergy: Enabling Secure Reputation-based Federated Learning in Smart Cities. IEEE Access, 12, 154035-154053. 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.

A New S-Box Design System for Data Encryption Using Artificial Bee Colony Algorithm (2023)
Journal Article
Yasin Ghadi, Y., Alshehri, M. S., Almakdi, S., Saidani, O., Alturki, N., Masood, F., & Shahbaz Khan, M. (2023). A New S-Box Design System for Data Encryption Using Artificial Bee Colony Algorithm. Computers, Materials & Continua, 77(1), 781-797. https://doi.org/10.32604/cmc.2023.042777

Securing digital image data is a key concern in today’s information-driven society. Effective encryption techniques are required to protect sensitive image data, with the Substitution-box (S-box) often playing a pivotal role in many symmetric encrypt... Read More about A New S-Box Design System for Data Encryption Using Artificial Bee Colony Algorithm.

Noise-Crypt: Image Encryption with Non-linear Noise, Hybrid Chaotic Maps, and Hashing (2023)
Presentation / Conference Contribution
Asghar, L., Ahmed, F., Khan, M. S., Arshad, A., & Ahmad, J. (2023, October). Noise-Crypt: Image Encryption with Non-linear Noise, Hybrid Chaotic Maps, and Hashing. Presented at 2023 International Conference on Engineering and Emerging Technologies (ICEET), Istanbul, Turkiye

To secure the digital images over insecure transmission channels, a new image encryption algorithm Noise-Crypt is proposed in this paper. Noise-Crypt integrates nonlinear random noise, hybrid chaotic maps, and SHA-256 hashing algorithm. The utilized... Read More about Noise-Crypt: Image Encryption with Non-linear Noise, Hybrid Chaotic Maps, and Hashing.

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.

Prediction of the SARS-CoV-2 Derived T-Cell Epitopes’ Response Against COVID Variants (2023)
Journal Article
Tahir, H., Shahbaz Khan, M., Ahmed, F., M. Albarrak, A., Noman Qasem, S., & Ahmad, J. (2023). Prediction of the SARS-CoV-2 Derived T-Cell Epitopes’ Response Against COVID Variants. Computers, Materials & Continua, 75(2), 3517-3535. https://doi.org/10.32604/cmc.2023.035410

The COVID-19 outbreak began in December 2019 and was declared a global health emergency by the World Health Organization. The four most dominating variants are Beta, Gamma, Delta, and Omicron. After the administration of vaccine doses, an eminent dec... Read More about Prediction of the SARS-CoV-2 Derived T-Cell Epitopes’ Response Against COVID Variants.

Predicting Breast Cancer Leveraging Supervised Machine Learning Techniques (2022)
Journal Article
Aamir, S., Rahim, A., Aamir, Z., Abbasi, S. F., Khan, M. S., Alhaisoni, M., Khan, M. A., Khan, K., & Ahmad, J. (2022). Predicting Breast Cancer Leveraging Supervised Machine Learning Techniques. Computational and Mathematical Methods in Medicine, 2022, Article 5869529. https://doi.org/10.1155/2022/5869529

Breast cancer is one of the leading causes of increasing deaths in women worldwide. The complex nature (microcalcification and masses) of breast cancer cells makes it quite difficult for radiologists to diagnose it properly. Subsequently, various com... Read More about Predicting Breast Cancer Leveraging Supervised Machine Learning Techniques.

Machine Learning-Based Prediction of Node Localization Accuracy in IIoT-Based MI-UWSNs and Design of a TD Coil for Omnidirectional Communication (2022)
Journal Article
Gang, Q., Muhammad, A., Khan, Z. U., Khan, M. S., Ahmed, F., & Ahmad, J. (2022). Machine Learning-Based Prediction of Node Localization Accuracy in IIoT-Based MI-UWSNs and Design of a TD Coil for Omnidirectional Communication. Sustainability, 14(15), Article 9683. https://doi.org/10.3390/su14159683

This study aims to realize Sustainable Development Goals (SDGs), i.e., SDG 9: Industry Innovation and Infrastructure and SDG 14: Life below Water, through the improvement of localization estimation accuracy in magneto-inductive underwater wireless se... Read More about Machine Learning-Based Prediction of Node Localization Accuracy in IIoT-Based MI-UWSNs and Design of a TD Coil for Omnidirectional Communication.

Addressing the Directionality Challenge through RSSI-Based Multilateration Technique, to Localize Nodes in Underwater WSNs by Using Magneto-Inductive Communication (2022)
Journal Article
Qiao, G., Muhammad, A., Muzzammil, M., Shoaib Khan, M., Tariq, M. O., & Khan, M. S. (2022). Addressing the Directionality Challenge through RSSI-Based Multilateration Technique, to Localize Nodes in Underwater WSNs by Using Magneto-Inductive Communication. Journal of marine science and engineering, 10(4), Article 530. https://doi.org/10.3390/jmse10040530

The deployment and efficient use of wireless sensor networks (WSNs) in underwater and underground environments persists to be a difficult task. In addition, the localization of a sensor Rx node in WSNs is an important aspect for the successful commun... Read More about Addressing the Directionality Challenge through RSSI-Based Multilateration Technique, to Localize Nodes in Underwater WSNs by Using Magneto-Inductive Communication.

RSSI based Trilateration Technique to Localize Nodes in Underwater Wireless Sensor Networks through Optical Communication (2021)
Presentation / Conference Contribution
Aman, M., Gang, Q., Mian, S., Muzzammil, M., Tariq, M. O., & Khan, M. S. (2021, December). RSSI based Trilateration Technique to Localize Nodes in Underwater Wireless Sensor Networks through Optical Communication. Presented at 2021 16th International Conference on Emerging Technologies (ICET), Islamabad, Pakistan

In the past few decades, optical communication with high data rates up to Gb/s in underwater wireless sensor networks (UWSN) has been extensively investigated. However, now a days researchers are highly focused on proposing/implementing localization... Read More about RSSI based Trilateration Technique to Localize Nodes in Underwater Wireless Sensor Networks through Optical Communication.

Detection of COVID-19 Using Transfer Learning and Grad-CAM Visualization on Indigenously Collected X-ray Dataset (2021)
Journal Article
Umair, M., Khan, M. S., Ahmed, F., Baothman, F., Alqahtani, F., Alian, M., & Ahmad, J. (2021). Detection of COVID-19 Using Transfer Learning and Grad-CAM Visualization on Indigenously Collected X-ray Dataset. Sensors, 21(17), Article 5813. https://doi.org/10.3390/s21175813

The COVID-19 outbreak began in December 2019 and has dreadfully affected our lives since then. More than three million lives have been engulfed by this newest member of the corona virus family. With the emergence of continuously mutating variants of... Read More about Detection of COVID-19 Using Transfer Learning and Grad-CAM Visualization on Indigenously Collected X-ray Dataset.