Dr Jawad Ahmad's Outputs (168)

Leveraging LLMs for Non-Security Experts in Threat Hunting: Detecting Living off the Land Techniques (2025)
Journal Article
Konstantinou, A., Kasimatis, D., Buchanan, W. J., Ullah Jan, S., Ahmad, J., Politis, I., & Pitropakis, N. (2025). Leveraging LLMs for Non-Security Experts in Threat Hunting: Detecting Living off the Land Techniques. Machine Learning and Knowledge Extraction, 7(2), Article 31. https://doi.org/10.3390/make7020031

This paper explores the potential use of Large Language Models (LLMs), such as ChatGPT, Google Gemini, and Microsoft Copilot, in threat hunting, specifically focusing on Living off the Land (LotL) techniques. LotL methods allow threat actors to blend... Read More about Leveraging LLMs for Non-Security Experts in Threat Hunting: Detecting Living off the Land Techniques.

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.

Sustainable Environmental Monitoring: Multistage Fusion Algorithm for Remotely Sensed Underwater Super-Resolution Image Enhancement and Classification (2024)
Journal Article
Ghaban, W., Ahmad, J., Siddique, A. A., Alshehri, M. S., Saghir, A., Saeed, F., Ghaleb, B., & Rehman, M. U. (2025). Sustainable Environmental Monitoring: Multistage Fusion Algorithm for Remotely Sensed Underwater Super-Resolution Image Enhancement and Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 18, 3640-3653. https://doi.org/10.1109/jstars.2024.3522202

Oceans and seas cover more than 70% of the Earth's surface. If compared with the land mass there are a lot of unexplored locations, a wealth of natural resources, and diverse ocean creatures that are inaccessible to us humans. Underwater rovers and v... Read More about Sustainable Environmental Monitoring: Multistage Fusion Algorithm for Remotely Sensed Underwater Super-Resolution Image Enhancement and Classification.

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.

Attention-Based Hybrid Deep Learning Model for Intrusion Detection in IIoT Networks (2024)
Journal Article
Ullah, S., Boulila, W., Koubaa, A., & Ahmad, J. (2024). Attention-Based Hybrid Deep Learning Model for Intrusion Detection in IIoT Networks. Procedia Computer Science, 246, 3323-3332. https://doi.org/10.1016/j.procs.2024.09.307

The integration of Industrial Internet of Things (IIoT) technology into the industrial sector has produced numerous significant advantages. However, the notable concern remains the absence of robust security and privacy measures in these interconnect... Read More about Attention-Based Hybrid Deep Learning Model for Intrusion Detection in IIoT Networks.

Transparent RFID tag wall enabled by artificial intelligence for assisted living (2024)
Journal Article
Khan, M. Z., Usman, M., Tahir, A., Farooq, M., Qayyum, A., Ahmad, J., Abbas, H., Imran, M., & Abbasi, Q. H. (2024). Transparent RFID tag wall enabled by artificial intelligence for assisted living. Scientific Reports, 14(1), 18896. https://doi.org/10.1038/s41598-024-64411-y

Current approaches to activity-assisted living (AAL) are complex, expensive, and intrusive, which reduces their practicality and end user acceptance. However, emerging technologies such as artificial intelligence and wireless communications offer new... Read More about Transparent RFID tag wall enabled by artificial intelligence for assisted living.

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.

Enhancing Cloud Computing Security Through Blockchain-Based Communication for Electronic Health Records (2024)
Presentation / Conference Contribution
Noyon, M. S. I., Moradpoor, N., Maglaras, L., & Ahmad, J. (2024, April). Enhancing Cloud Computing Security Through Blockchain-Based Communication for Electronic Health Records. Presented at DCOSS-IoT 2024, Abu Dhabi, United Arab Emirates

The health sector stands as one of the most crucial and vulnerable domains, harbouring extensive personal data. Particularly, Electronic Health Records store information in electronic media where users lack control over their data. Unauthorized acces... Read More about Enhancing Cloud Computing Security Through Blockchain-Based Communication for Electronic Health Records.

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.

ML-Driven Attack Detection in RPL Networks: Exploring Attacker Position's Significance (2024)
Presentation / Conference Contribution
Ghaleb, B., Al-Dubai, A., Romdhani, I., Ahmad, J., Aldhaheri, T., & Kulkarni, S. (2024, January). ML-Driven Attack Detection in RPL Networks: Exploring Attacker Position's Significance. Presented at 2024 International Conference on Information Networking (ICOIN), Ho Chi Minh City, Vietnam

The Routing Protocol for Low Power and Lossy Networks (RPL) plays a pivotal role in IoT communication, employing a rank-based topology to guide routing decisions. However, RPL is vulnerable to Decreased Rank Attacks, where malicious nodes illegitimat... Read More about ML-Driven Attack Detection in RPL Networks: Exploring Attacker Position's Significance.

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.

Clustering-Based Resource Management for Consumer Cost Optimization in IoT Edge Computing Environments (2024)
Journal Article
Denden, M., Jemmali, M., Boulila, W., Soni, M., Khan, F., & Ahmad, J. (online). Clustering-Based Resource Management for Consumer Cost Optimization in IoT Edge Computing Environments. IEEE Transactions on Consumer Electronics, https://doi.org/10.1109/tce.2024.3414929

Edge computing emerges as a pivotal model in the era of next-generation consumer electronics and the emerging challenges of multimodal data-driven decision-making. Specifically, edge computing offers an open computing architecture for the vast and di... Read More about Clustering-Based Resource Management for Consumer Cost Optimization in IoT Edge Computing Environments.

SACNN‐IDS: A self‐attention convolutional neural network for intrusion detection in industrial internet of things (2024)
Journal Article
Qathrady, M. A., Ullah, S., Alshehri, M. S., Ahmad, J., Almakdi, S., Alqhtani, S. M., Khan, M. A., & Ghaleb, B. (2024). SACNN‐IDS: A self‐attention convolutional neural network for intrusion detection in industrial internet of things. CAAI Transactions on Intelligence Technology, 9(6), 1398-1411. https://doi.org/10.1049/cit2.12352

Industrial Internet of Things (IIoT) is a pervasive network of interlinked smart devices that provide a variety of intelligent computing services in industrial environments. Several IIoT nodes operate confidential data (such as medical, transportatio... Read More about SACNN‐IDS: A self‐attention convolutional neural network for intrusion detection in industrial internet of things.

ML-FAS: Multi-Level Face Anonymization Scheme and Its Application to E-Commerce Systems (2024)
Journal Article
Jiang, D., Ahmad, J., Suo, Z., Alsulami, M. M., Ghadi, Y. Y., & Boulila, W. (2024). ML-FAS: Multi-Level Face Anonymization Scheme and Its Application to E-Commerce Systems. IEEE Transactions on Consumer Electronics, 70(3), 5090 - 5100. https://doi.org/10.1109/tce.2024.3411102

With the proliferation of electronic commerce, the facial data used for identity authentication and mobile payment are potentially subject to data analytics and mining attacks by third-party platforms, which has raised public privacy concerns. To tac... Read More about ML-FAS: Multi-Level Face Anonymization Scheme and Its Application to E-Commerce Systems.

A Two-branch Edge Guided Lightweight Network for infrared image saliency detection (2024)
Journal Article
Liu, Z., Li, X., Zhang, T., Zhang, X., Sun, C., Rehman, S. U., & Ahmad, J. (2024). A Two-branch Edge Guided Lightweight Network for infrared image saliency detection. Computers and Electrical Engineering, 118(Part A), Article 109296. https://doi.org/10.1016/j.compeleceng.2024.109296

In the dynamic landscape of saliency detection, convolutional neural networks have emerged as catalysts for innovation, but remain largely tailored for RGB imagery, falling short in the context of infrared images, particularly in memory-restricted en... Read More about A Two-branch Edge Guided Lightweight Network for infrared image saliency detection.

ACNN-IDS: An Attention-Based CNN for Cyberattack Detection in IoT (2024)
Presentation / Conference Contribution
Huma, Z. E., Ahmad, J., Hamadi, H. A., Ghaleb, B., Buchanan, W. J., & Jan, S. U. (2024, February). ACNN-IDS: An Attention-Based CNN for Cyberattack Detection in IoT. Presented at 2024 2nd International Conference on Cyber Resilience (ICCR), Dubai, United Arab Emirates

The Internet of Things (IoT) has become an integral part of modern societies, with devices, networks, and applications offering industrial, economic, and social benefits. However, these devices and networks generate vast amounts of data, making them... Read More about ACNN-IDS: An Attention-Based CNN for Cyberattack Detection in IoT.

A transformer-based approach empowered by a self-attention technique for semantic segmentation in remote sensing (2024)
Journal Article
Boulila, W., Ghandorh, H., Masood, S., Alzahem, A., Koubaa, A., Ahmed, F., Khan, Z., & Ahmad, J. (2024). A transformer-based approach empowered by a self-attention technique for semantic segmentation in remote sensing. Heliyon, 10(8), Article e29396. https://doi.org/10.1016/j.heliyon.2024.e29396

Semantic segmentation of Remote Sensing (RS) images involves the classification of each pixel in a satellite image into distinct and non-overlapping regions or segments. This task is crucial in various domains, including land cover classification, au... Read More about A transformer-based approach empowered by a self-attention technique for semantic segmentation in remote sensing.

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 Self-Attention-Based Deep Convolutional Neural Networks for IIoT Networks Intrusion Detection (2024)
Journal Article
Alshehri, M. S., Saidani, O., Alrayes, F. S., Abbasi, S. F., & Ahmad, J. (2024). A Self-Attention-Based Deep Convolutional Neural Networks for IIoT Networks Intrusion Detection. IEEE Access, 12, 45762-45772. https://doi.org/10.1109/access.2024.3380816

The Industrial Internet of Things (IIoT) comprises a variety of systems, smart devices, and an extensive range of communication protocols. Hence, these systems face susceptibility to privacy and security challenges, making them prime targets for mali... Read More about A Self-Attention-Based Deep Convolutional Neural Networks for IIoT Networks Intrusion Detection.